Error suppression in sequenced DNA fragments using redundant reads with unique molecular indices (UMIS)

ABSTRACT

The disclosed embodiments concern methods, apparatus, systems and computer program products for determining sequences of interest using unique molecular index (UMI) sequences that are uniquely associable with individual polynucleotide fragments, including sequences with low allele frequencies and long sequence length. In some implementations, the UMIs include both physical UMIs and virtual UMIs. In some implementations, the unique molecular index sequences include non-random sequences. System, apparatus, and computer program products are also provided for determining a sequence of interest implementing the methods disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefits under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/153,699, filed Apr. 28, 2015, U.S.Provisional Patent Application No. 62/193,469, filed Jul. 16, 2015, andU.S. Provisional Patent Application No. 62/269,485, filed Dec. 18, 2015,which are herein incorporated by reference in their entireties for allpurposes.

SEQUENCE LISTING

The instant application contains a Sequence Listing which is submittedelectronically in ASCII format and is hereby incorporated by referencein its entirety. Said ASCII copy, created on Apr. 15, 2016, is namedILMNP008_ST25.txt and is 2 KB in size.

BACKGROUND

Next generation sequencing technology is providing increasingly highspeed of sequencing, allowing larger sequencing depth. However, becausesequencing accuracy and sensitivity are affected by errors and noisefrom various sources, e.g., sample defects, PCR during librarypreparation, enrichment, clustering, and sequencing, increasing depth ofsequencing alone cannot ensure detection of sequences of very low allelefrequency, such as in fetal cell-free DNA (cfDNA) in maternal plasma,circulating tumor DNA (ctDNA), sub-clonal mutations in pathogens.Therefore, it is desirable to develop methods for determining sequencesof DNA molecules in small quantity and/or low allele frequency whilesuppressing sequencing inaccuracy due to various sources of errors.

SUMMARY

The disclosed implementations concern methods, apparatus, systems, andcomputer program products for determining nucleic acid fragmentsequences using unique molecular indices (UMIs). In variousimplementations, sequencing methods determine the sequences of nucleicacid fragments from both strands of the nucleic acid fragments. In someimplementations, the methods employ physical UMIs located on one or bothstrands of sequencing adapters. In some implementations, the methodsalso employ virtual UMIs located on both strands of the nucleic acidfragments.

One aspect of the disclosure relates to a method for sequencing nucleicacid molecules from a sample using unique molecular indices (UMIs). Eachunique molecular index (UMI) is an oligonucleotide sequence that can beused to identify an individual molecule of a double-stranded DNAfragment in the sample. The method include: (a) applying adapters toboth ends of double-stranded DNA fragments in the sample, wherein theadapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a physical UMI onone strand or each strand of the adapters, thereby obtaining DNA-adapterproducts; (b) amplifying both strands of the DNA-adapter products toobtain a plurality of amplified polynucleotides; (c) sequencing theplurality of amplified polynucleotides, thereby obtaining a plurality ofreads each associated with a physical UMI; (d) identifying a pluralityof physical UMIs associated with the plurality of reads; (e) identifyinga plurality of virtual UMIs associated with the plurality of reads,wherein each virtual UMI is a sequence found in a DNA fragment in thesample; and (f) determining sequences of the double-stranded DNAfragments in the sample using the plurality of reads obtained in (c),the plurality of physical UMIs identified in (d), and the plurality ofvirtual UMIs identified in (e). In some implementations, the methodinclude operation (f) includes: (i) combining, for each of one or moreof the double-stranded DNA fragments in the sample, (1) reads having afirst physical UMI and at least one virtual UMI in the 5′ to 3′direction and (2) reads having a second physical UMI and the at leastone virtual UMI in the 5′ to 3′ direction to determine a consensusnucleotide sequence; and (ii) determining, for each of the one or moreof the double-stranded DNA fragments in the sample, a sequence using theconsensus nucleotide sequence.

In some implementations, the plurality of physical UMIs includes randomUMIs. In some implementations, the plurality of physical UMIs includesnonrandom UMIs. In some implementations, every nonrandom UMI differsfrom every other nonrandom UMI of the adapters by at least twonucleotides at corresponding sequence positions of the nonrandom UMIs.In some implementations, the plurality of physical UMIs includes no morethan about 10,000, about 1,000, about 500, or about 100 unique nonrandomUMIs. In some implementations, the plurality of physical UMIs includesabout 96 unique nonrandom UMIs.

In some implementations of the methods above, applying adapters to bothends of double-stranded DNA fragments includes ligating the adapters toboth ends of the double stranded DNA fragments. In some implementations,operation (f) includes using reads sharing a common physical UMI and acommon virtual UMI to determine a sequence of a DNA fragment of thesample.

In some implementations of the methods above, the plurality of physicalUMIs includes fewer than 12 nucleotides. In some implementations, theplurality of UMIs includes no more than 6 nucleotides. In someimplementations, the plurality of UMIs includes no more than 4nucleotides.

In some implementations, the adapters each include a physical UMI oneach strand of the adapters in the double-stranded hybridized region. Insome implementations, the physical UMI is at an end of thedouble-stranded hybridized region, said end of the double-strandedhybridized region being opposite from the 3′ arm or the 5′ arm, or isone nucleotide away from said end of the double-stranded hybridizedregion. In some implementations, the adapters each include a 5′-TGG-3′trinucleotide or a 3′-ACC-5′ trinucleotide on the double-strandedhybridized region adjacent to a physical UMI. In some implementations,the adapters each include a read primer sequence on each strand of thedouble-stranded hybridized region.

In some implementations, the adapters each include a physical UMI ononly one strand of the adapters on the single-stranded 5′ arm or thesingle-stranded 3′ arm. In some of these implementation, (f) includes:(i) collapsing reads having a same first physical UMI into a first groupto obtain a first consensus nucleotide sequence; (ii) collapsing readshaving a same second physical UMI into a second group to obtain a secondconsensus nucleotide sequence; and (iii) determining, using the firstand second consensus nucleotide sequences, a sequence of one of thedouble-stranded DNA fragments in the sample. In some implementations,(iii) includes: (1) obtaining, using localization information andsequence information of the first and second consensus nucleotidesequences, a third consensus nucleotide sequence, and (2) determining,using the third consensus nucleotide sequence, the sequence of one ofthe double-stranded DNA fragments. In some implementations, operation(e) includes identifying the plurality of virtual UMIs, while theadapters each include the physical UMI on only one strand of theadapters in the single-stranded 5′ arm region or the single-stranded 3′arm region. In some implementations, (f) includes: (i) combining readshaving a first physical UMI and at least one virtual UMI in the 5′ to 3′direction and reads having a second physical UMI and the at least onevirtual UMI in the 5′ to 3′ direction to determine a consensusnucleotide sequence; and (ii) determining a sequence of one of thedouble-stranded DNA fragments in the sample using the consensusnucleotide sequence.

In some implementations, the adapters each include a physical UMI oneach strand of the adapters in a double-stranded region of the adapters,wherein the physical UMI on one strand is complementary to the physicalUMI on the other strand. In some implementations, operation (f)includes: (i) combining reads having a first physical UMI, at least onevirtual UMI, and a second physical UMI in the 5′ to 3′ direction andreads having the second physical UMI, the at least one virtual UMI, andthe first physical UMI in the 5′ to 3′ direction to determine aconsensus nucleotide sequence; and (ii) determining a sequence of one ofthe double-stranded DNA fragments in the sample using the consensusnucleotide sequence.

In some implementations, the adapters each include a first physical UMIon a 3′ arm of the adapter and a second physical UMI on a 5′ arm of theadapter, wherein the first physical UMI and the second physical UMI arenot complementary to each other. In some of such implementations, (f)includes: (i) combining reads having a first physical UMI, at least onevirtual UMI, and a second physical UMI in the 5′ to 3′ direction andreads having a third physical UMI, the at least one virtual UMI, and afourth physical UMI in the 5′ to 3′ direction to determine a consensusnucleotide sequence; and (ii) determining a sequence of one of thedouble-stranded DNA fragments in the sample using the consensusnucleotide sequence.

In some implementations, at least some of the virtual UMIs derive fromsubsequences at or near the ends of the double-stranded DNA fragments inthe sample.

In some implementations, one or more physical UMIs and/or one or morevirtual UMIs are uniquely associated with a double-stranded DNA fragmentin the sample.

In some implementations, the double-stranded DNA fragments in the sampleinclude more than about 1,000 DNA fragments.

In some implementations, the plurality of virtual UMIs include UMIs ofabout 6 bp to about 24 bp. In some implementations, the plurality ofvirtual UMIs include UMIs of about 6 bp to about 10 bp.

In some implementations of the methods above, obtaining the plurality ofreads in operation (c) includes: obtaining two pair-end reads from eachof the amplified polynucleotides, where in the two pair-end readsinclude a long read and a short read, the long read being longer thanthe short read. In some of these implementations, operation (f)includes: combining read pairs associated with a first physical UMI intoa first group and combining read pairs associated with a second physicalUMI into a second group, wherein the first and the second physical UMIsare uniquely associated with a double-stranded fragment in the sample;and determining the sequence of the double-stranded fragment in thesample using sequence information of long reads in the first group andsequence information of long reads in the second group. In someimplementations, the long read has a read length of about 500 bp ormore. In some implementations, the short read has a read length of about50 bp or less.

In some implementations, the method suppresses errors arise in one ormore of the following operations: PCR, library preparation, clustering,and sequencing.

In some implementations, the amplified polynucleotides include an allelehaving an allele frequency lower than about 1%.

In some implementations, the amplified polynucleotides include a cellfree DNA molecule originating from a tumor, and the allele is indicativeof the tumor.

In some implementations, sequencing the plurality of amplifiedpolynucleotides includes obtaining reads having at least about 100 bp.

Another aspect of the instant disclosure relates to a method forsequencing nucleic acid molecules from a sample, including (a) attachingadapters to both ends of double-stranded DNA fragments in the sample,wherein the adapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a physical uniquemolecular index (UMI) on the single-stranded 5′ arm or thesingle-stranded 3′ arm; (b) amplifying both strands of ligation productsfrom (a), thereby obtaining a plurality of single-stranded, amplifiedpolynucleotides; (c) sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads each associatedwith a physical UMI; (d) identifying a plurality of physical UMIsassociated with the plurality of reads; and (e) determining sequences ofthe double-stranded DNA fragments in the sample using the plurality ofsequences obtained in (c) and the plurality of physical UMIs identifiedin (d).

An additional aspect of the disclosure relates to a method forsequencing nucleic acid molecules from a sample. The method includes:(a) attaching adapters to both ends of double-stranded DNA fragments inthe sample, wherein the adapters each include a double-strandedhybridized region, a single-stranded 5′ arm, a single-stranded 3′ arm,and a physical unique molecular index (UMI) shorter than 12 nucleotideson one strand or each strand of the adapters; (b) amplifying bothstrands of ligation products from (a), thereby obtaining a plurality ofsingle-stranded, amplified polynucleotides each including a physicalUMI; (c) sequencing the plurality of amplified polynucleotides, therebyobtaining a plurality of reads each associated with a physical UMI; (d)identifying a plurality of physical UMIs associated with the pluralityof reads; and (e) determining sequences of the double-stranded DNAfragments in the sample using the plurality of reads obtained in (c) andthe plurality of physical UMIs identified in (d).

Another aspect of the instant disclosure relates a method for making aduplex sequencing adapter having a physical UMI on each strand. Themethod includes: providing a preliminary sequencing adapter including adouble-stranded hybridized region, two single-stranded arms, and anoverhang including 5′-CCANNNNANNNNTGG-3′ at an end of thedouble-stranded hybridized region that is further away from the twosingle stranded arms; extending one strand of the double-strandedhybridized region using the overhang as a template, thereby producing anextension product; and applying restriction enzyme Xcm1 to digest adouble-stranded end of the extension product, thereby producing theduplex sequencing adapter having a physical UMI on each strand. In someimplementations, the preliminary sequencing adapter includes a readprimer sequence on each strand.

A further aspect of the instant disclosure relates to a computer programproduct including a non-transitory machine readable medium storingprogram code that, when executed by one or more processors of a computersystem, causes the computer system to implement a method for determiningsequence information of a sequence of interest in a sample using uniquemolecular indices (UMIs). The program code includes: (a) code forobtaining reads of a plurality of amplified polynucleotides, wherein theplurality of amplified polynucleotides are obtained by amplifyingdouble-stranded DNA fragments in the sample including the sequence ofinterest and attaching adapters to the double-stranded DNA fragments;(b) code for identifying a plurality of physical UMIs in the reads ofthe plurality of amplified polynucleotides, wherein each physical UMI isfound in an adapter attached to one of the double-stranded DNAfragments; (c) code for identifying a plurality of virtual UMIs in thereceived reads of the plurality of amplified polynucleotides, whereineach virtual UMI is found in an individual molecule of one of thedouble-stranded DNA fragments; and (c) code for determining sequences ofthe double-stranded DNA fragments using the reads of the plurality ofamplified polynucleotides, the plurality of physical UMIs, and theplurality of virtual UMIs, thereby reducing errors in the determinedsequences of the double-stranded DNA fragments. In some implementations,the adapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a physical uniquemolecular index (UMI) on one strand or each strand of the adapters.

In some implementations, the code for determining sequences of thedouble-stranded DNA fragments includes: (i) code for collapsing readshaving a same first physical UMI into a first group to obtain a firstconsensus nucleotide sequence; (ii) code for collapsing reads having asame second physical UMI into a second group to obtain a secondconsensus nucleotide sequence; and (iii) code for determining, using thefirst and second consensus nucleotide sequences, a sequence of one ofthe double-stranded DNA fragments in the sample.

In some implementations, the code for determining sequences of thedouble-stranded DNA fragments includes: (i) code for combining sequencereads having a first physical UMI, at least one virtual UMI, and asecond physical UMI in the 5′ to 3′ direction and sequence reads havingthe second physical UMI, the at least one virtual UMI, and the firstphysical UMI in the 5′ to 3′ direction to determine a consensusnucleotide sequence; and (ii) code for determining a sequence of one ofthe double-stranded DNA fragments in the sample using the consensusnucleotide sequence.

An additional aspect of the disclosure relates to a computer system,including: one or more processors; system memory; and one or morecomputer-readable storage media. The media has stored thereoncomputer-executable instructions that causes the computer system toimplement a method to determine sequence information of a sequence ofinterest in a sample using unique molecular indices (UMIs), which areoligonucleotide sequences that can be used to identify individualmolecules of double-stranded DNA fragments in the sample. Theinstructions includes: (a) receiving reads of a plurality of amplifiedpolynucleotides, wherein the plurality of amplified polynucleotides areobtained by amplifying double-stranded DNA fragments in the sampleincluding the sequence of interest and attaching adapters to thedouble-stranded DNA fragments; (b) identifying a plurality of physicalUMIs in the received reads of the plurality of amplifiedpolynucleotides, wherein each physical UMI is found in an adapterattached to one of the double-stranded DNA fragments; (c) identifying aplurality of virtual UMIs in the received reads of the plurality ofamplified polynucleotides, wherein each virtual UMI is found in anindividual molecule of one of the double-stranded DNA fragments; and (d)determining sequences of the double-stranded DNA fragments using thesequences of the plurality of amplified polynucleotides, the pluralityof physical UMIs, and the plurality of virtual UMIs, thereby reducingerrors in the determined sequences of the double-stranded DNA fragments.

In some implementations, determining sequences of the double-strandedDNA fragments includes: (i) collapsing reads having a same firstphysical UMI into a first group to obtain a first consensus nucleotidesequence; (ii) collapsing reads having a same second physical UMI into asecond group to obtain a second consensus nucleotide sequence; and (iii)determining, using the first and second consensus nucleotide sequences,a sequence of one of the double-stranded DNA fragments.

In some implementations, determining sequences of the double-strandedDNA fragments includes: (i) combining reads having a first physical UMI,at least one virtual UMI, and a second physical UMI in the 5′ to 3′direction and reads having the second physical UMI, the at least onevirtual UMI, and the first physical UMI in the 5′ to 3′ direction todetermine a consensus nucleotide sequence; and (ii) determining asequence of one of the double-stranded DNA fragments using the consensusnucleotide sequence.

One aspect of the disclosure provides methods for sequencing nucleicacid molecules from a sample using nonrandom unique molecular indices(UMIs). The methods involve: (a) applying adapters to both ends of DNAfragments in the sample, wherein the adapters each include adouble-stranded hybridized region, a single-stranded 5′ arm, asingle-stranded 3′ arm, and a nonrandom unique molecular index (UMI) onone strand or each strand of the adapters, thereby obtaining DNA-adapterproducts; (b) amplifying the DNA-adapter products to obtain a pluralityof amplified polynucleotides; (c) sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads associated witha plurality of nonrandom UMIs; (d) from the plurality of reads,identifying reads sharing a common nonrandom UMI; and (e) from theidentified reads sharing the common nonrandom UMI, determining thesequence of at least a portion of a DNA fragment, from the sample,having an applied adaptor with the common non-random UMI.

In some implementations, a method further involves: from the readssharing the common nonrandom UMI, selecting reads sharing both thecommon nonrandom UMI and a common read position, where determining thesequence of the DNA fragment in (e) uses only reads sharing both thecommon nonrandom UMI and the common read position in a referencesequence. In some implementations, every nonrandom UMI differs fromevery other nonrandom UMI by at least two nucleotides at correspondingsequence positions of the nonrandom UMIs.

Another aspect of the disclosure relates to methods for sequencingnucleic acid molecules from a sample using nonrandom unique molecularindices (UMIs). In some implementations, a method involves: (a) applyingadapters to both ends of double-stranded DNA fragments in the sample,wherein the adapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a nonrandom uniquemolecular index (UMI) on one strand or each strand of the adapters,thereby obtaining DNA-adapter products, wherein the nonrandom UMI can becombined with other information to uniquely identify an individualmolecule of the double-stranded DNA fragments; (b) amplifying bothstrands of the DNA-adapter products to obtain a plurality of amplifiedpolynucleotides; (c) sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads each associatedwith a nonrandom UMI; (d) identifying a plurality of nonrandom UMIsassociated with the plurality of reads; and (e) using the plurality ofreads and the plurality of nonrandom UMIs to determine sequences of thedouble-stranded DNA fragments in the sample.

In some implementations, using the plurality of reads and the pluralityof nonrandom UMIs to determine the sequences of the double-stranded DNAfragments in the sample involves: identifying reads sharing a commonnonrandom UMI, and using the identified reads to determine a sequence ofa DNA fragment in the sample. In some implementations, using theplurality of reads and the plurality of nonrandom UMIs to determine thesequences of the double-stranded DNA fragments in the sample involves:identifying reads sharing a common nonrandom UMI and a common readposition, and using the identified reads to determine a sequence of aDNA fragment in the sample.

In some implementations, using the plurality of reads and the pluralityof nonrandom UMIs to determine sequences of the double-stranded DNAfragments in the sample involves: identifying reads sharing a commonnonrandom UMI and a common virtual UMI, wherein the common virtual UMIis found in a DNA fragment in the sample; and using the identified readsto determine a sequence of the DNA fragment in the sample.

In some implementations, using the plurality of reads and the pluralityof nonrandom UMIs to determine sequences of the double-stranded DNAfragments in the sample involves: identifying reads sharing a commonnonrandom UMI, a common read position, and a common virtual UMI, whereinthe common virtual UMI is found in a DNA fragment in the sample; andusing the identified reads to determine a sequence of the DNA fragmentin the sample.

In some implementations, every nonrandom UMI differs from every othernonrandom UMI of the adapters by at least two nucleotides atcorresponding sequence positions of the nonrandom UMIs. In someimplementations, the adapters each include a physical UMI on each strandof the adapters in the double-stranded hybridized region. In someimplementations, the plurality of nonrandom UMIs includes no more thanabout 10,000, about 1,000, or about 100 unique nonrandom UMIs. In someimplementations, the plurality of nonrandom UMIs includes about 96unique nonrandom UMIs.

In some implementations, the plurality of reads each includes anonrandom UMI. In some implementations, the plurality of reads eacheither includes a nonrandom UMI or is associated with a nonrandom UMIthrough a paired-end read. In some implementations, the plurality ofamplified polynucleotides each has a nonrandom UMI on one end or has afirst nonrandom UMI on a first end and a second nonrandom UMI on asecond end.

System, apparatus, and computer program products are also provided fordetermining DNA fragment sequences implementing the methods disclosed.

One aspect of the disclosure provides a computer program productincluding a non-transitory machine readable medium storing program codethat, when executed by one or more processors of a computer system,causes the computer system to implement a method to determine sequenceinformation of a sequence of interest in a sample using unique molecularindices (UMIs). The program code includes instructions to perform themethods above.

Although the examples herein concern humans and the language isprimarily directed to human concerns, the concepts described herein areapplicable to nucleic acids from any virus, plant, animal, or otherorganism, and to populations of the same (metagenomes, viralpopulations, etc.) These and other features of the present disclosurewill become more fully apparent from the following description, withreference to the figures, and the appended claims, or may be learned bythe practice of the disclosure as set forth hereinafter.

INCORPORATION BY REFERENCE

All patents, patent applications, and other publications, including allsequences disclosed within these references, referred to herein areexpressly incorporated herein by reference, to the same extent as ifeach individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by reference.All documents cited are, in relevant part, incorporated herein byreference in their entireties for the purposes indicated by the contextof their citation herein. However, the citation of any document is notto be construed as an admission that it is prior art with respect to thepresent disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a flow chart illustrating an example workflow using UMIs tosequence nucleic acid fragments.

FIG. 1B shows a DNA fragment/molecule and the adapters employed ininitial steps of workflow shown in FIG. 1A.

FIG. 2A schematically illustrates five different adapter designs thatmay be adopted in the various implementations.

FIG. 2B illustrates a hypothetical process in which UMI jumping occursin a PCR reaction involving adapters having two physical UMIs on twoarms.

FIG. 2C shows a process for making adapters having UMIs on both strandsof the adapters in the double-stranded region, which process uses a15-mer sequence (SEQ ID NO:1) as a recognition sequence for restrictionenzyme Xcm1.

FIG. 2D shows a diagram of an adapter having a P7 arm top strand (SEQ IDNO:2) and a P5 arm bottom strand (SEQ ID NO:3).

FIG. 2E schematically illustrates a nonrandom UMI design that provides amechanism for detecting errors that occur in the UMI sequence during asequencing process.

FIGS. 3A and 3B are diagrams showing the materials and reaction productsof ligating adapters to double stranded fragments according to somemethods disclosed herein.

FIGS. 4A-4E illustrates how methods as disclosed herein can suppressdifferent sources of error in determining the sequence of a doublestranded DNA fragment.

FIG. 5 schematically illustrates applying physical UMIs and virtual UMIsto efficiently obtain long pair end reads.

FIG. 6 is a block diagram of a dispersed system for processing a testsample.

FIG. 7A and FIG. 7B show experimental data demonstrating theeffectiveness of error suppression using the methods disclosed herein.

FIG. 8 shows data indicating that using position information alone tocollapse reads tends to collapse reads that are actually derived fromdifferent source molecules.

FIG. 9 plots empirical data showing that using nonrandom UMI andposition information to collapse reads may provide more accurateestimates of fragments than using position information alone.

FIG. 10 shows different errors occur in three samples processed withrandom UMIs in tabular form.

FIG. 11A shows sensitivity and selectivity of calling somatic mutationand CNV in a gDNA sample using the two collapsing methods with twodifferent tools: VarScan and Denovo,

FIGS. 11B-D show selectivity (i.e., false positive rate) of callingsomatic mutation and CNV in three cfDNA samples having increasing sampleinputs using the two collapsing methods with two different tools:VarScan and Denovo.

DETAILED DESCRIPTION

The disclosure concerns methods, apparatus, systems, and computerprogram products for sequencing nucleic acids, especially nucleic acidswith limited quantity or low concentration, such as fetal cfDNA inmaternal plasma or circulating tumor DNA (ctDNA) in a cancer patient'sblood.

Unless otherwise indicated, the practice of the methods and systemsdisclosed herein involves conventional techniques and apparatus commonlyused in molecular biology, microbiology, protein purification, proteinengineering, protein and DNA sequencing, and recombinant DNA fields thatare within the skill of the art. Such techniques and apparatus are knownto those of skill in the art and are described in numerous texts andreference works (See e.g., Sambrook et al., “Molecular Cloning: ALaboratory Manual,” Third Edition (Cold Spring Harbor), [2001]).

Numeric ranges are inclusive of the numbers defining the range. It isintended that every maximum numerical limitation given throughout thisspecification includes every lower numerical limitation, as if suchlower numerical limitations were expressly written herein. Every minimumnumerical limitation given throughout this specification will includeevery higher numerical limitation, as if such higher numericallimitations were expressly written herein. Every numerical range giventhroughout this specification will include every narrower numericalrange that falls within such broader numerical range, as if suchnarrower numerical ranges were all expressly written herein.

The headings provided herein are not intended to limit the disclosure.

Unless defined otherwise herein, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art. Various scientific dictionaries that include the termsincluded herein are well known and available to those in the art.Although any methods and materials similar or equivalent to thosedescribed herein find use in the practice or testing of the embodimentsdisclosed herein, some methods and materials are described.

The terms defined immediately below are more fully described byreference to the Specification as a whole. It is to be understood thatthis disclosure is not limited to the particular methodology, protocols,and reagents described, as these may vary, depending upon the contextthey are used by those of skill in the art.

Definitions

As used herein, the singular terms “a,” “an,” and “the” include theplural reference unless the context clearly indicates otherwise.

Unless otherwise indicated, nucleic acids are written left to right in5′ to 3′ orientation and amino acid sequences are written left to rightin amino to carboxy orientation, respectively.

Unique molecular indices (UMIs) are sequences of nucleotides applied toor identified in DNA molecules that may be used to distinguishindividual DNA molecules from one another. Since UMIs are used toidentify DNA molecules, they are also referred to as unique molecularidentifiers. See, e.g., Kivioja, Nature Methods 9, 72-74 (2012). UMIsmay be sequenced along with the DNA molecules with which they areassociated to determine whether the read sequences are those of onesource DNA molecule or another. The term “UMI” is used herein to referto both the sequence information of a polynucleotide and the physicalpolynucleotide per se.

Commonly, multiple instances of a single source molecule are sequenced.In the case of sequencing by synthesis using Illumina's sequencingtechnology, the source molecule may be PCR amplified before delivery toa flow cell. Whether or not PCR amplified, the individual DNA moleculesapplied to flow cell are bridge amplified or ExAmp amplified to producea cluster. Each molecule in a cluster derives from the same source DNAmolecule but is separately sequenced. For error correction and otherpurposes, it can be important to determine that all reads from a singlecluster are identified as deriving from the same source molecule. UMIsallow this grouping. A DNA molecule that is copied by amplification orotherwise to produce multiple instances of the DNA molecule is referredto as a source DNA molecule.

UMIs are similar to bar codes, which are commonly used to distinguishreads of one sample from reads of other samples, but UMIs are insteadused to distinguish one source DNA molecule from another when many DNAmolecules are sequenced together. Because there may be many more DNAmolecules in a sample than samples in a sequencing run, there aretypically many more distinct UMIs than distinct barcodes in a sequencingrun.

As mentioned, UMIs may be applied to or identified in individual DNAmolecules. In some implementations, the UMIs may be applied to the DNAmolecules by methods that physically link or bond the UMIs to the DNAmolecules, e.g., by ligation or transposition through polymerase,endonuclease, transposases, etc. These “applied” UMIs are therefore alsoreferred to as physical UMIs. In some contexts, they may also bereferred to as exogenous UMIs. The UMIs identified within source DNAmolecules are referred to as virtual UMIs. In some context, virtual UMIsmay also be referred to as endogenous UMI.

Physical UMIs may be defined in many ways. For example, they may berandom, pseudo-random or partially random, or non-random nucleotidesequences that are inserted in adapters or otherwise incorporated insource DNA molecules to be sequenced. In some implementations, thephysical UMIs may be so unique that each of them is expected to uniquelyidentify any given source DNA molecule present in a sample. Thecollection of adapters is generated, each having a physical UMI, andthose adapters are attached to fragments or other source DNA moleculesto be sequenced, and the individual sequenced molecules each has a UMIthat helps distinguish it from all other fragments. In suchimplementations, a very large number of different physical UMIs (e.g.,many thousands to millions) may be used to uniquely identify DNAfragments in a sample.

Of course, the physical UMI must have a sufficient length to ensure thisuniqueness for each and every source DNA molecule. In someimplementations, a less unique molecular identifier can be used inconjunction with other identification techniques to ensure that eachsource DNA molecule is uniquely identified during the sequencingprocess. In such implementations, multiple fragments or adapters mayhave the same physical UMI. Other information such as alignment locationor virtual UMIs may be combined with the physical UMI to uniquelyidentify reads as being derived from a single source DNAmolecule/fragment. In some implementations, adaptors include physicalUMIs limited to a relatively small number of nonrandom sequences, e.g.,96 nonrandom sequences. Such physical UMIs are also referred to asnonrandom UMIs. In some implementations, the nonrandom UMIs may becombined with sequence position information and/or virtual UMIs toidentify reads attributable to a same source DNA molecule. Theidentified reads may be collapsed to obtain a consensus sequence thatreflects the sequence of the source DNA molecule as described herein.

A “virtual unique molecular index” or “virtual UMI” is a uniquesub-sequence in a source DNA molecule. In some implementations, virtualUMIs are located at or near the ends of the source DNA molecule. One ormore such unique end positions may alone or in conjunction with otherinformation uniquely identify a source DNA molecule. Depending on thenumber of distinct source DNA molecules and the number of nucleotides inthe virtual UMI, one or more virtual UMIs can uniquely identify sourceDNA molecules in a sample. In some cases, a combination of two virtualunique molecular identifiers is required to identify a source DNAmolecule. Such combinations may be extremely rare, possibly found onlyonce in a sample. In some cases, one or more virtual UMIs in combinationwith one or more physical UMIs may together uniquely identify a sourceDNA molecule.

A “random UMI” may be considered a physical UMI selected as a randomsample, with or without replacement, from a set of UMIs consisting ofall possible different oligonucleotide sequences given one or moresequence lengths. For instance, if each UMI in the set of UMIs has nnucleotides, then the set includes 4{circumflex over ( )}n UMIs havingsequences that are different from each other. A random sample selectedfrom the 4{circumflex over ( )}n UMIs constitutes a random UMI.

Conversely, a “nonrandom UMI” as used herein refers to a physical UMIthat is not a random UMI. In some embodiments, available nonrandom UMIsare predefined for a particular experiment or application. In certainembodiments, rules are used to generate sequences for a set or select asample from the set to obtain a nonrandom UMI. For instance, thesequences of a set may be generated such that the sequences have aparticular pattern or patterns. In some implementations, each sequencediffers from every other sequence in the set by a particular number of(e.g., 2, 3, or 4) nucleotides. That is, no nonrandom UMI sequence canbe converted to any other available nonrandom UMI sequence by replacingfewer than the particular number of nucleotides. In someimplementations, a nonrandom UMI is selected from a set of UMIsincluding fewer than all possible UMIs given a particular sequencelength. For instance, a nonrandom UMI having 6 nucleotides may beselected from a total of 96 different sequences (instead of a total of4{circumflex over ( )}6=4096 possible different sequences). In otherimplementations, sequences are not randomly selected from a set.Instead, some sequences are selected with higher probability than othersequences.

In some implementations where nonrandom UMIs are selected from a setwith fewer than all possible different sequences, the number ofnonrandom UMIs is fewer, sometimes significantly so, than the number ofsource DNA molecules. In such implementations, nonrandom UMI informationmay be combined with other information, such as virtual UMI and/orsequence information, to identify sequence reads deriving from a samesource DNA molecule.

The term “paired end reads” refers to reads obtained from paired endsequencing that obtains one read from each end of a nucleic fragment.Paired end sequencing involves fragmenting DNA into sequences calledinserts. In some protocols such as some used by Illumina, the reads fromshorter inserts (e.g., on the order of tens to hundreds of bp) arereferred to as short-insert paired end reads or simply paired end reads.In contrast, the reads from longer inserts (e.g., on the order ofseveral thousands of bp) are referred to as mate pair reads. In thisdisclosure, short-insert paired end reads and long-insert mate pairreads may both be used and are not differentiated with regard to theprocess for determining sequences of DNA fragments. Therefore, the term“paired end reads” may refer to both short-insert paired end reads andlong-insert mate pair reads, which are further described herein after.In some embodiments, paired end reads include reads of about 20 bp to1000 bp. In some embodiments, paired end reads include reads of about 50bp to 500 bp, about 80 bp to 150 bp, or about 100 bp.

As used herein, the terms “alignment” and “aligning” refer to theprocess of comparing a read to a reference sequence and therebydetermining whether the reference sequence contains the read sequence.An alignment process attempts to determine if a read can be mapped to areference sequence, but does not always result in a read aligned to thereference sequence. If the reference sequence contains the read, theread may be mapped to the reference sequence or, in certain embodiments,to a particular location in the reference sequence. In some cases,alignment simply tells whether or not a read is a member of a particularreference sequence (i.e., whether the read is present or absent in thereference sequence). For example, the alignment of a read to thereference sequence for human chromosome 13 will tell whether the read ispresent in the reference sequence for chromosome 13. A tool thatprovides this information may be called a set membership tester. In somecases, an alignment additionally indicates a location in the referencesequence where the read maps to. For example, if the reference sequenceis the whole human genome sequence, an alignment may indicate that aread is present on chromosome 13, and may further indicate that the readis on a particular strand and/or site of chromosome 13. In somescenarios, alignment tools are imperfect, in that a) not all validalignments are found, and b) some obtained alignments are invalid. Thishappens due to various reasons, e.g., reads may contain errors, andsequenced reads may be different from the reference genome due tohaplotype differences. In some applications, the alignment tools includebuilt-in mismatch tolerance, which tolerates certain degrees of mismatchof base pairs and still allow alignment of reads to a referencesequence. This can help to identify valid alignment of reads that wouldotherwise be missed.

Aligned reads are one or more sequences that are identified as a matchin terms of the order of their nucleic acid molecules to a knownreference sequence such as a reference genome. An aligned read and itsdetermined location on the reference sequence constitute a sequence tag.Alignment can be done manually, although it is typically implemented bya computer algorithm, as it would be impossible to align reads in areasonable time period for implementing the methods disclosed herein.One example of an algorithm from aligning sequences is the EfficientLocal Alignment of Nucleotide Data (ELAND) computer program distributedas part of the Illumina Genomics Analysis pipeline. Alternatively, aBloom filter or similar set membership tester may be employed to alignreads to reference genomes. See U.S. patent application Ser. No.14/354,528, filed Apr. 25, 2014, which is incorporated herein byreference in its entirety. The matching of a sequence read in aligningcan be a 100% sequence match or less than 100% (i.e., a non-perfectmatch).

The term “mapping” used herein refers to assigning a read sequence to alarger sequence, e.g., a reference genome, by alignment.

The terms “polynucleotide,” “nucleic acid” and “nucleic acid molecules”are used interchangeably and refer to a covalently linked sequence ofnucleotides (i.e., ribonucleotides for RNA and deoxyribonucleotides forDNA) in which the 3′ position of the pentose of one nucleotide is joinedby a phosphodiester group to the 5′ position of the pentose of the next.The nucleotides include sequences of any form of nucleic acid,including, but not limited to RNA and DNA molecules such as cell-freeDNA (cfDNA) molecules. The term “polynucleotide” includes, withoutlimitation, single- and double-stranded polynucleotides.

The term “test sample” herein refers to a sample, typically derived froma biological fluid, cell, tissue, organ, or organism, that includes anucleic acid or a mixture of nucleic acids having at least one nucleicacid sequence that is to be screened for copy number variation and othergenetic alterations, such as, but not limited to, single nucleotidepolymorphism, insertions, deletions, and structural variations. Incertain embodiments the sample has at least one nucleic acid sequencewhose copy number is suspected of having undergone variation. Suchsamples include, but are not limited to sputum/oral fluid, amnioticfluid, blood, a blood fraction, or fine needle biopsy samples, urine,peritoneal fluid, pleural fluid, and the like. Although the sample isoften taken from a human subject (e.g., a patient), the assays can beused for samples from any mammal, including, but not limited to dogs,cats, horses, goats, sheep, cattle, pigs, etc., as well as mixedpopulations, as microbial populations from the wild, or viralpopulations from patients. The sample may be used directly as obtainedfrom the biological source or following a pretreatment to modify thecharacter of the sample. For example, such pretreatment may includepreparing plasma from blood, diluting viscous fluids, and so forth.Methods of pretreatment may also involve, but are not limited to,filtration, precipitation, dilution, distillation, mixing,centrifugation, freezing, lyophilization, concentration, amplification,nucleic acid fragmentation, inactivation of interfering components, theaddition of reagents, lysing, etc. If such methods of pretreatment areemployed with respect to the sample, such pretreatment methods aretypically such that the nucleic acid(s) of interest remain in the testsample, sometimes at a concentration proportional to that in anuntreated test sample (e.g., namely, a sample that is not subjected toany such pretreatment method(s)). Such “treated” or “processed” samplesare still considered to be biological “test” samples with respect to themethods described herein.

The term “Next Generation Sequencing (NGS)” herein refers to sequencingmethods that allow for massively parallel sequencing of clonallyamplified molecules and of single nucleic acid molecules. Non-limitingexamples of NGS include sequencing-by-synthesis using reversible dyeterminators, and sequencing-by-ligation.

The term “read” refers to a sequence read from a portion of a nucleicacid sample. Typically, though not necessarily, a read represents ashort sequence of contiguous base pairs in the sample. The read may berepresented symbolically by the base pair sequence in A, T, C, and G ofthe sample portion, together with a probabilistic estimate of thecorrectness of the base (quality score). It may be stored in a memorydevice and processed as appropriate to determine whether it matches areference sequence or meets other criteria. A read may be obtaineddirectly from a sequencing apparatus or indirectly from stored sequenceinformation concerning the sample. In some cases, a read is a DNAsequence of sufficient length (e.g., at least about 20 bp) that can beused to identify a larger sequence or region, e.g., that can be alignedand mapped to a chromosome or genomic region or gene.

The terms “site” and “alignment location” are used interchangeably torefer to a unique position (i.e. chromosome ID, chromosome position andorientation) on a reference genome. In some embodiments, a site may be aresidue's, a sequence tag's, or a segment's position on a referencesequence.

As used herein, the term “reference genome” or “reference sequence”refers to any particular known genome sequence, whether partial orcomplete, of any organism or virus which may be used to referenceidentified sequences from a subject. For example, a reference genomeused for human subjects as well as many other organisms is found at theNational Center for Biotechnology Information at ncbi.nlm.nih.gov. A“genome” refers to the complete genetic information of an organism orvirus, expressed in nucleic acid sequences. However, it is understoodthat “complete” is a relative concept, because even the gold-standardreference genome are expected to include gaps and errors.

In various embodiments, the reference sequence is significantly largerthan the reads that are aligned to it. For example, it may be at leastabout 100 times larger, or at least about 1000 times larger, or at leastabout 10,000 times larger, or at least about 10⁵ times larger, or atleast about 10⁶ times larger, or at least about 10⁷ times larger.

In one example, the reference sequence is that of a full length humangenome. Such sequences may be referred to as genomic referencesequences. In another example, the reference sequence is limited to aspecific human chromosome such as chromosome 13. In some embodiments, areference Y chromosome is the Y chromosome sequence from human genomeversion hg19. Such sequences may be referred to as chromosome referencesequences. Other examples of reference sequences include genomes ofother species, as well as chromosomes, sub-chromosomal regions (such asstrands), etc., of any species.

In some embodiments, a reference sequence for alignment may have asequence length from about 1 to about 100 times the length of a read. Insuch embodiments, the alignment and sequencing are considered a targetedalignment or sequencing, instead of a whole genome alignment orsequencing. In these embodiments, the reference sequence typicallyincludes a gene sequence and/or other constrained sequence of interest.

In various embodiments, the reference sequence is a consensus sequenceor other combination derived from multiple individuals. However, incertain applications, the reference sequence may be taken from aparticular individual.

The term “derived” when used in the context of a nucleic acid or amixture of nucleic acids, herein refers to the means whereby the nucleicacid(s) are obtained from the source from which they originate. Forexample, in one embodiment, a mixture of nucleic acids that is derivedfrom two different genomes means that the nucleic acids, e.g., cfDNA,were naturally released by cells through naturally occurring processessuch as necrosis or apoptosis. In another embodiment, a mixture ofnucleic acids that is derived from two different genomes means that thenucleic acids were extracted from two different types of cells from asubject.

The term “biological fluid” herein refers to a liquid taken from abiological source and includes, for example, blood, serum, plasma,sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears,saliva, and the like. As used herein, the terms “blood,” “plasma” and“serum” expressly encompass fractions or processed portions thereof.Similarly, where a sample is taken from a biopsy, swab, smear, etc., the“sample” expressly encompasses a processed fraction or portion derivedfrom the biopsy, swab, smear, etc.

As used herein the term “chromosome” refers to the heredity-bearing genecarrier of a living cell, which is derived from chromatin strandsincluding DNA and protein components (especially histones). Theconventional internationally recognized individual human genomechromosome numbering system is employed herein.

As used herein, the term “polynucleotide length” refers to the absolutenumber of nucleic acid molecules (nucleotides) in a sequence or in aregion of a reference genome. The term “chromosome length” refers to theknown length of the chromosome given in base pairs, e.g., provided inthe NCBI36/hg18 assembly of the human chromosome found at|genome|.|ucsc|.|edu/cgi-bin/hgTracks?hgsid=167155613&chromInfoPage= onthe World Wide Web.

The term “primer,” as used herein refers to an isolated oligonucleotidethat is capable of acting as a point of initiation of synthesis whenplaced under conditions inductive to synthesis of an extension product(e.g., the conditions include nucleotides, an inducing agent such as DNApolymerase, necessary ions and molecules, and a suitable temperature andpH). The primer may be preferably single stranded for maximum efficiencyin amplification, but alternatively may be double stranded. If doublestranded, the primer is first treated to separate its strands beforebeing used to prepare extension products. The primer may be anoligodeoxyribonucleotide. The primer is sufficiently long to prime thesynthesis of extension products in the presence of the inducing agent.The exact lengths of the primers will depend on many factors, includingtemperature, source of primer, use of the method, and the parametersused for primer design.

Introduction and Context

Next generation sequencing (NGS) technology has developed rapidly,providing new tools to advance research and science, as well ashealthcare and services relying on genetic and related biologicalinformation. NGS methods are performed in a massively parallel fashion,affording increasingly high speed for determining biomolecules sequenceinformation. However, many of the NGS methods and associated samplemanipulation techniques introduce errors such that the resultingsequences have relatively high error rate, ranging from one error in afew hundred base pairs to one error in a few thousand base pairs. Sucherror rates are sometimes acceptable for determining inheritable geneticinformation such as germline mutations because such information isconsistent across most somatic cells, which provide many copies of thesame genome in a test sample. An error originating from reading one copyof a sequence has a minor or removable impact when many copies of thesame sequence are read without error. For instance, if an erroneous readfrom one copy of a sequence cannot be properly aligned to a referencesequence, it may simply be discarded from analysis. Error-free readsfrom other copies of the same sequence may still provide sufficientinformation for valid analyses. Alternatively, instead of discarding theread having a base pair different from other reads from the samesequence, one can disregard the different base pair as resulting from aknown or unknown source of error.

However, such error correction approaches do not work well for detectingsequences with low allele frequencies, such as sub-clonal, somaticmutations found in nucleic acids from tumor tissue, circulating tumorDNA, low-concentration fetal cfDNA in maternal plasma, drug-resistantmutations of pathogens, etc. In these examples, one DNA fragment mayharbor a somatic mutation of interest at a sequence site, while manyother fragments at the same sequence site do not have the mutation ofinterest. In such a scenario, the sequence reads or base pairs from themutated DNA fragment might be unused or misinterpreted in conventionalsequencing, thereby losing information for detecting the mutation ofinterest.

Due to these various sources of errors, increasing depth of sequencingalone cannot ensure detection of somatic variations with very low allelefrequency (e.g., <1%). Some implementations disclosed herein provideduplex sequencing methods that effectively suppress errors in situationswhen signals of valid sequences of interest are low, such as sampleswith low allele frequencies. The methods use virtual unique molecularindices (UMIs) in conjunction with short physical unique molecularindices placed on one arm or both arms of sequencing adapters, such asthe Illumina TruSeq® adapter. These implementations are based on thestrategy of using physical UMIs on adapter sequences and virtual UMIs onsample DNA fragment sequences. In some implementations, alignmentpositions of reads are also used to suppress errors. For example, whenmultiple reads (or pairs of reads) share a physical UMI and align withinthe same interval (constrained range of positions) on the reference, thereads are expected to originate from a single DNA fragment. PhysicalUMIs, virtual UMIs, and alignment positions associated with readsprovide “indices” that are, alone or in combination, uniquely associatedwith a specific double stranded DNA fragment from a sample. Using theseindices, one can identify multiple reads derived from a single DNAfragment (a single molecule), which may be just one of many fragmentsfrom the same genomic site. Using the multiple reads from a single DNAmolecule, error correction can be performed effectively. For example,the sequencing methodology may obtain a consensus nucleotide sequence(hereinafter referred to as “a consensus sequence”) from the multiplereads derived from the same DNA fragment, which correction does notdiscard valid sequence information of this DNA fragment.

Adapter designs can provide physical UMIs that allow one to determinewhich strand of the DNA fragment the reads are derived from. Someembodiments take advantage of this to determine a first consensussequence for reads derived from one strand of the DNA fragment, and asecond consensus sequence for the complementary strand. In manyembodiments, a consensus sequence includes the base pairs detected inall or a majority of reads while excluding base pairs appearing in fewof the reads. Different criteria of consensus may be implemented. Theprocess of combining reads based on UMIs or alignment locations toobtain a consensus sequence is also referred to as “collapsing” thereads. Using physical UMIs, virtual UMIs, and/or alignment locations,one can determine that reads for the first and second consensussequences are derived from the same double stranded fragment. Therefore,in some embodiments, a third consensus sequence is determined using thefirst and second consensus sequences obtained for the same DNAmolecule/fragment, with the third consensus sequence including basepairs common for the first and second consensus sequences whileexcluding those inconsistent between the two. In alternativeimplementations, only one consensus sequence may be directly obtained bycollapsing all reads derived from both strands of the same fragment,instead of by comparing the two consensus sequences obtained from thetwo strands. Finally, the sequence of the fragment may be determinedfrom the third or the only one consensus sequence, which includes basepairs that are consistent across reads derived from both strands of thefragment.

Various implementations combine reads of two strands of a DNA fragmentto suppress errors. However, in some implementations, the method appliesphysical and virtual UMIs to single-stranded nucleic acid (e.g., DNA orRNA) fragments, and combine reads sharing the same physical and virtualUMIs to suppress errors. Various methods may be employed to capturesingle stranded nucleic acid fragments in a sample.

In some embodiments, the method combines different types of indices todetermine the source polynucleotide on which reads are derived. Forexample, the method may use both physical and virtual UMIs to identifyreads deriving from a single DNA molecule. By using a second form ofUMI, in addition to the physical UMI, the physical UMIs may be shorterthan when only physical UMIs are used to determine the sourcepolynucleotide. This approach has minimal impact on library prepperformance, and does not require extra sequencing read length.

Applications of the Disclosed Methods Include:

-   -   Error suppression for somatic mutation detection. For example,        detection of mutation with less than 0.1% allele frequency is        highly critical in liquid biopsy of circulating tumor DNA.    -   Correct prephasing, phasing and other sequencing errors to        achieve high quality long reads (e.g., 1×1000 bp)    -   Decrease cycle time for fixed read length, and correct increased        phasing and prephasing by this method.    -   Use UMIs on both sides of fragment to create virtual long paired        end reads. For example, stitch a 2×500 read by doing 500+50 on        duplicates.        Example Workflow for Sequencing Nucleic Acid Fragments Using        UMIs

FIG. 1A is a flow chart illustrating an example workflow 100 for usingUMIs to sequence nucleic acid fragments. Operation 102 providesfragments of double-stranded DNA. The DNA fragments may be obtained byfragmenting genomic DNA, collecting naturally fragmented DNA (e.g.,cfDNA or ctDNA), or synthesizing DNA fragments from RNA, for example. Insome implementations, to synthesize DNA fragments from RNA, messengerRNA is first purified using polyA selection or depletion of ribosomalRNA, then the selected mRNA is chemically fragmented and converted intosingle-stranded cDNA using random hexamer priming. A complementarystrand of the cDNA is generated to create a double-stranded cDNA that isready for library construction. To obtain double stranded DNA fragmentsfrom genomic DNA (gDNA), input gDNA is fragmented, e.g., by hydrodynamicshearing, nebulization, enzymatic fragmentation, etc., to generatefragments of appropriate lengths, e.g., about 1000 bp, 800 bp, 500, or200 bp. For instance, nebulization can break up DNA into pieces lessthan 800 bp in short periods of time. This process generatesdouble-stranded DNA fragments containing 3′ and/or 5′ overhangs.

FIG. 1B shows a DNA fragment/molecule and the adapters employed ininitial steps of workflow 100 in FIG. 1A. Although only onedouble-stranded fragment is illustrated in FIG. 1B, thousands tomillions of fragments of a sample can be prepared simultaneously in theworkflow. DNA fragmentation by physical methods produces heterogeneousends, including a mixture of 3′ overhangs, 5′ overhangs, and blunt ends.The overhangs will be of varying lengths and ends may or may not bephosphorylated. An example of the double-stranded DNA fragments obtainedfrom fragmenting genomic DNA of operation 102 is shown as fragment 123in FIG. 1B.

Fragment 123 has both a 3′ overhang on the left end and a 5′ overhangshown on the right end, and is marked with ρ and φ, indicating twosequences in the fragment that may be used as virtual UMIs, which, whenused alone or combined with physical UMIs of an adapter to be ligated tothe fragment, may uniquely identify the fragment. UMIs are uniquelyassociated with a single DNA fragment in a sample including a sourcepolynucleotide and its complementary strand. A physical UMI is asequence of an oligonucleotide linked to the source polynucleotide, itscomplementary strand, or a polynucleotide derived from the sourcepolynucleotide. A virtual UMI is a sequence of an oligonucleotide withinthe source polynucleotide, its complementary strand, or a polynucleotidederived from the source polynucleotide. Within this scheme, one may alsorefer to the physical UMI as an extrinsic UMI, and the virtual UMI as anintrinsic UMI.

The two sequences ρ and φ actually each refer to two complementarysequences at the same genomic site, but for simplicity sake, they areindicated on only one strand in some of the double-stranded fragmentsshown herein. Virtual UMIs such as ρ and φ can be used at a later stepof the workflow to help identify reads originating from one or bothstrands of the single DNA source fragment. With the reads so identified,they can be collapsed to obtain a consensus sequence.

If DNA fragments are produced by physical methods, workflow 100 proceedsto perform end repair operation 104, which produces blunt-end fragmentshaving 5′-phosphorylated ends. In some implementations, this stepconverts the overhangs resulting from fragmentation into blunt endsusing T4 DNA polymerase and Klenow enzyme. The 3′ to 5′ exonucleaseactivity of these enzymes removes 3′ overhangs and the 5′ to 3′polymerase activity fills in the 5′ overhangs. In addition, T4polynucleotide kinase in this reaction phosphorylates the 5′ ends of theDNA fragments. The fragment 125 in FIG. 1B is an example of anend-repaired, blunt-end product.

After end repairing, workflow 100 proceeds to operation 106 to adenylate3′ ends of the fragments, which is also referred to as A-tailing ordA-tailing, because a single dATP is added to the 3′ ends of the bluntfragments to prevent them from ligating to one another during theadapter ligation reaction. Double stranded molecule 127 of FIG. 1B showsan A-tailed fragment having blunt ends with 3′-dA overhangs and5′-phosphate ends. A single ‘T’ nucleotide on the 3′ end of each of thetwo sequencing adapters as seen in item 129 of FIG. 1B provides anoverhang complementary to the 3′-dA overhang on each end of the insertfor ligating the two adapters to the insert.

After adenylating 3′ ends, workflow 100 proceeds to operation 108 toligate partially double stranded adapters to both ends of the fragments.In some implementations, the adapters used in a reaction includeoligonucleotides that are all different from each other, whicholigonucleotides provide physical UMIs to associate sequence reads to asingle source polynucleotide, which may be a single- or double-strandedDNA fragment. Because all the physical UMI oligonucleotides aredifferent, the two UMI oligonucleotides ligated to two ends of aparticular fragment are different from each other. Furthermore, the twophysical UMIs for the particular fragment are different from thephysical UMIs for every other fragment. In this regard, the two physicalUMIs are uniquely associated with the particular fragment.

Item 129 of FIG. 1B illustrates two adapters to be ligated to thedouble-stranded fragment that includes two virtual UMIs ρ and φ near theends of the fragment. These adapters are illustrated based on thesequencing adapters of the Illumina platform, as various implementationsmay use Illumina's NGS platform to obtain reads and detect sequence ofinterest. The adapter shown on the left includes the physical UMI α onits P5 arm, while the adapter on the right includes physical UMI β onits P5 arm. On the strand having the 5′ denatured end, from 5′ to 3′direction, adapters have a P5 sequence, a physical UMI (α or β), and aread 2 primer sequence. On the strand having the 3′ denatured end, from3′ to 5′ direction, the adapters have a P7′ sequence, an index sequence,and a read 1 primer sequence. The P5 and P7′ oligonucleotides arecomplementary to the amplification primers bound to the surface of flowcells of Illumina sequencing platform. In some implementations, theindex sequence provides a means to keep track of the source of a sample,thereby allowing multiplexing of multiple samples on the sequencingplatform. Other designs of adapters and sequencing platforms may be usedin various implementations. Adapters and sequencing technology arefurther described in sections that follow. The reaction depicted in FIG.1B adds distinct sequences to the 5′ and 3′ ends of each strand in thegenomic fragment. A ligation product 131 from the same fragmentdescribed above is illustrated in FIG. 1B. This ligation product 131 hasthe physical UMI α, the virtual UMI ρ and the virtual UMI φ on its topstrand, in the 5′-3′ direction. The ligation product also has thephysical UMI β, the virtual UMI φ and the virtual UMI ρ on its bottomstrand, in the 5′-3′ direction. The ligation product and the physicalUMIs and virtual UMIs contained therein shown in 132 are similar tothose in the top half of FIG. 3A. This disclosure embodies methods usingsequencing technologies and adapters other than those provided byIllumina.

In some implementations, the products of this ligation reaction arepurified and/or size-selected by agarose gel electrophoresis or magneticbeads. Size-selected DNA is then PCR amplified to enrich for fragmentsthat have adapters on both ends. See block 110. The bottom half of FIG.3A illustrates that both strands of ligation product undergo PCRamplification, yielding two families of fragments having differentphysical UMIs (α and β). The two families each have only one physicalUMI. The two families both have virtual UMIs ρ and φ, but the orders ofthe virtual UMIs with reference to physical UMIs are different: α-ρ-φversus β-φ-ρ. Some implementations purify PCR products and select asize-range of templates appropriate for subsequent cluster generation.

Workflow 100 then proceeds to cluster amplify PCR products on anIllumina platform. See operation 112. By clustering of the PCR products,libraries can be pooled for multiplexing, e.g., with up to 12 samplesper lane, using different index sequences on the adapters to keep trackof different samples.

After cluster amplification, sequencing reads can be obtained throughsequencing by synthesis on the Illumina platform. See operation 114.Although the adapters and the sequencing process described here arebased on the Illumina platform, others sequencing technologies,especially NGS methods may be used instead of or in addition to theIllumina platform.

The sequencing reads derived from the segment shown in FIGS. 1B and 3Aare also expected to include UMIs α-ρ-φ or β-φ-ρ. The workflow 100 usesthis feature to collapse reads having the same physical UMI(s) and/orthe same virtual UMI(s) into one or more groups, thereby obtaining oneor more consensus sequences. See operation 116. A consensus sequenceincludes nucleotide bases that are consistent or meet a consensuscriterion across reads in a collapsed group. As shown in operation 116,physical UMIs, virtual UMIs, and position information may be combined invarious ways to collapse reads to obtain consensus sequences fordetermining the sequence of a fragment or at least a portion thereof. Insome implementations, physical UMIs are combined with virtual UMIs tocollapse reads. In other implementations, physical UMIs and readpositions are combined to collapse reads. Read position information maybe obtained by various techniques using different position measurements,e.g., genomic coordinates of the reads, positions on a referencesequence, or chromosomal positions. In further implementations, physicalUMIs, virtual UMIs, and read positions are combined to collapse reads.

Finally, workflow 100 uses the one or more consensus sequences todetermine the sequence of the nucleic acid fragment from the sample. Seeoperation 118. This may involve determining the nucleic acid fragment'ssequence as the third consensus sequence or the single consensussequence described above.

In a particular implementation that includes operations similar tooperations 108-119, a method for sequencing nucleic acid molecules froma sample using nonrandom UMIs involves the following: (a) applyingadapters to both ends of DNA fragments in the sample, wherein theadapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a nonrandom UMI,thereby obtaining DNA-adapter products; (b) amplifying the DNA-adapterproducts to obtain a plurality of amplified polynucleotides; (c)sequencing the plurality of amplified polynucleotides, thereby obtaininga plurality of reads associated with a plurality of nonrandom UMIs; (d)from the plurality of reads, identifying reads sharing a commonnonrandom UMI and a common read position; and (e) from the identifiedreads, determining the sequence of at least a portion of a DNA fragment.

In various implementations, obtained sequence reads are associated withphysical UMIs (e.g., random or nonrandom UMIs). In such implementations,a UMI is either part of a read sequence or part of a different read'ssequence, where the different read and the read in question are known tocome from the same fragment; e.g., by pair end reading or locationspecific information. Such as virtual UMIs.

In some implementations, the sequence reads are paired-end reads. Eachread either includes a nonrandom UMI or is associated with a nonrandomUMI through a paired-end read. In some implementations, the read lengthsare shorter than the DNA fragments or shorter than one half of thefragments' length. In such cases, the complete sequence of the wholefragment is sometimes not determined. Rather, the two ends of thefragment are determined. For example, a DNA fragment may be 500 bp long,from which two 100 bp paired-end reads can be derived. In this example,the 100 bases at each end of the fragment can be determined, and the 300bp in the middle of the fragment may not be determined without usinginformation of other reads. In some implementations, if the two pair-endreads are long enough to overlap, the complete sequence of the wholefragment may be determined from the two reads. For instance, see theexample described in association with FIG. 5.

In some implementations, every nonrandom UMI differs from every othernonrandom UMI by at least two nucleotides at corresponding sequencepositions of the nonrandom UMIs. In various implementations, theplurality of nonrandom UMIs includes no more than about 10,000, 1,000,or 100 unique nonrandom UMIs. In some implementations, the plurality ofnonrandom UMIs includes 96 unique nonrandom UMIs.

In some implementations, an adaptor has a duplex nonrandom UMI in thedouble stranded region of the adaptor, and each read includes a firstnonrandom UMI on one end and a second nonrandom UMI on the other end.

Adapters and UMIs

Adapters

In addition to the adapter design described in the example workflowabove, other designs of adapters may be used in various implementationsof the methods and systems disclosed herein. FIG. 2A schematicallyillustrates five different designs of adapter with UMI(s) that may beadopted in the various implementations.

FIG. 2A(i) shows a standard Illumina TruSeq® dual index adapter. Theadapter is partially double-stranded and is formed by annealing twooligonucleotides corresponding to the two strands. The two strands havea number of complementary base pairs (e.g., 12-17 bp) that allow the twooligonucleotides to anneal at the end to be ligated with a dsDNAfragment. A dsDNA fragment to be ligated on both ends for pair-end readsis also referred to as an insert. Other base pairs are not complementaryon the two strands, resulting in a fork-shaped adapter having two floppyoverhangs. In the example of FIG. 2A(i), the complementary base pairsare part of read 2 primer sequence and read 1 primer sequence.Downstream to the read 2 primer sequence is a single nucleotide 3′-Toverhang, which provides an overhang complementary to the singlenucleotide 3′-A overhang of a dsDNA fragment to be sequenced, which canfacilitate hybridization of the two overhangs. The read 1 primersequence is at the 5′ end of the complementary strand, to which aphosphate group is attached. The phosphate group facilitates ligatingthe 5′ end of the read 1 primer sequence to the 3′-A overhang of the DNAfragment. On the strand having the 5′ floppy overhang (the top strand),from 5′ to 3′ direction, the adapter has a P5 sequence, i5 indexsequence, and the read 2 primer sequence. On the strand having the 3′floppy overhang, from 3′ to 5′ direction, the adapter has a P7′sequence, an i7 index sequence, and the read 1 primer sequence. The P5and P7′ oligonucleotides are complementary to the amplification primersbound to the surface of flow cells of an Illumina sequencing platform.In some implementations, the index sequences provide means to keep trackof the source of a sample, thereby allowing multiplexing of multiplesamples on the sequencing platform.

FIG. 2A(ii) shows an adapter having a single physical UMI replacing thei7 index region of the standard dual index adapter shown in FIG. 2A(i).This design of the adapter mirrors that shown in the example workflowdescribed above in association with FIG. 1B. In certain embodiments, thephysical UMIs α and β are designed to be on only the 5′ arm of thedouble-stranded adapters, resulting in ligation products that have onlyone physical UMI on each strand. In comparison, physical UMIsincorporated into both strands of the adapters result in ligationproducts that have two physical UMIs on each strand, doubling the timeand cost to sequence the physical UMIs. However, this disclosureembodies methods employing physical UMIs on both strands of the adaptersas depicted in FIG. 2A(iii)-2A(vi), which provide additional informationthat may be utilized for collapsing different reads to obtain consensussequences.

In some implementations, the physical UMIs in the adapters includerandom UMIs. In some implementations, the physical UMIs in the adaptersinclude nonrandom UMIs.

FIG. 2A(iii) shows an adapter having two physical UMIs added to thestandard dual index adapter. The physical UMIs shown here may be randomUMIs or nonrandom UMIs. The first physical UMI is upstream to the i7index sequence, and the second physical UMI is upstream to the i5 indexsequence. FIG. 2A(iv) shows an adapter also having two physical UMIsadded to the standard dual index adapter. The first physical UMI isdownstream to the i7 index sequence, and the second physical UMI isdownstream to the i5 index sequence. Similarly, the two physical UMIsmay be random UMIs or nonrandom UMIs.

An adapter having two physical UMIs on the two arms of the singlestranded region, such as those shown in 2A(iii) and 2A(iv), may link twostrands of a double stranded DNA fragment, if a priori or a posterioriinformation associating the two un-complementary physical UMIs is known.For instance, a researcher may know the sequences of UMI 1 and UMI 2before integrating them to the same adapter in the designed shown inFIG. 2A(iv). This association information may be used to infer thatreads having UMI 1 and UMI 2 derive from two strands of the DNA fragmentto which the adapter was ligated. Therefore, one may collapse not onlyreads having the same physical UMI, but also reads having either of thetwo un-complementary physical UMIs. Interestingly, and as discussedbelow, a phenomenon referred to as “UMI jumping” may complicate theinference of association among physical UMIs on single-stranded regionsof adapters.

The two physical UMIs on the two strands of the adapters in FIG. 2A(iii)and FIG. 2A(iv) are neither located at the same site nor complementaryto each other. However, this disclosure embodies methods employingphysical UMIs that are at the same site on two strands of the adapterand/or complementary to each other. FIG. 2A(v) shows a duplex adapter inwhich the two physical UMIs are complementary on a double strandedregion at or near the end of the adapter. In some implementations, aphysical UMI near the end of the adapter may be 1 nucleotide, 2nucleotides, 3 nucleotides, 4 nucleotides, 5 nucleotides, or about 10nucleotides from an end of the double-stranded region of the adapter,the end being opposite from the forked region of the adapter. The twophysical UMIs may be random UMIs or nonrandom UMIs. FIG. 2A(vi) shows anadapter similar to but shorter than that of FIG. 2A(v), but it does notinclude the index sequences or the P5 and P7′ sequences complementary toflow cell surface amplification primers. Similarly, the two physicalUMIs may be random UMIs or nonrandom UMIs.

Compared to adapters having one or more single-stranded physical UMIs onsingle-stranded arms, adapters having a double-stranded physical UMI onthe double-stranded region can provide a direct link between two strandsof a double stranded DNA fragment to which the adapter is ligated, asshown in FIG. 2A(v) and FIG. 2A(vi). Since the two strands of adouble-stranded physical UMI are complementary to each other, theassociation between the two strands of the double-stranded UMI isinherently reflected by the complementary sequences, and can beestablished without requiring either a priori or a posterioriinformation. This information may be used to infer that reads having thetwo complementary sequences of a double-stranded physical UMI of anadapter are derived from the same DNA fragment to which the adapter wasligated, but the two complementary sequences of the physical UMI areligated to the 3′ end on one strand and the 5′ end on the other strandof the DNA fragment. Therefore, one may collapse not only reads havingthe same order of two physical UMI sequences on two ends, but also readshaving the reverse order of two complementary sequences on two ends.

In some embodiments, it can be advantageous to employ relatively shortphysical UMIs because short physical UMIs are easier to incorporate intoadapters. Furthermore, shorter physical UMIs are faster and easier tosequence in the amplified fragments. However, as physical UMIs becomevery short, the total number of different physical UMIs can become lessthan the number of adapter molecules required for sample processing. Inorder to provide enough adapters, the same UMI would have to be repeatedin two or more adapter molecules. In such a scenario, adapters havingthe same physical UMIs may be ligated to multiple source DNA molecules.However, these short physical UMIs may provide enough information, whencombined with other information such as virtual UMIs and/or alignmentlocations of reads, to uniquely identify reads as being derived from aparticular source polynucleotide or DNA fragment in a sample. This is sobecause even though the same physical UMI may be ligated to twodifferent fragments, it is unlikely the two different fragments wouldalso happen to have the same alignment locations, or matchingsubsequences serving as virtual UMIs. So if two reads have the sameshort physical UMI and the same alignment location (or the same virtualUMI), the two reads are likely derived from the same DNA fragment.

Furthermore, in some implementations, read collapsing is based on twophysical UMIs on the two ends of an insert. In such implementations, twovery short physical UMIs (e.g., 4 bp) are combined to determine thesource of DNA fragments, the combined length of the two physical UMIsproviding sufficient information for distinguishing among differentfragments.

In various implementations, physical UMIs are about 12 base pairs orshorter, about 11 base pairs or shorter, about 10 base pairs or shorter,about 9 base pairs or shorter, about 8 base pairs or shorter, about 7base pairs or shorter, about 6 base pairs or shorter, about 5 base pairsor shorter, about 4 base pairs or shorter, or about 3 base pairs orshorter. In some implementations where the physical UMIs are nonrandomUMIs, the UMIs are about 12 base pairs or shorter, about 11 base pairsor shorter, about 10 base pairs or shorter, about 9 base pairs orshorter, about 8 base pairs or shorter, about 7 base pairs or shorter,or about 6 base pairs.

UMI jumping may affect the inference of association among physical UMIson one arm or both arms of adapters, such as in the adapters of FIG.2A(ii)-(iv). It has been observed that when applying these adapters toDNA fragments, amplification products may include a larger number offragments having unique physical UMIs than the actual number offragments in the sample.

Furthermore, when adapters having physical UMIs on both arms areapplied, amplified fragments having a common physical UMI on one end aresupposed to have another common physical UMI on another end. However,sometimes this is not the case. For instance, in the reaction product ofone amplification reaction, some fragments may have a first physical UMIand a second physical UMI on their two ends; other fragments may havethe second physical UMI and a third physical UMI; yet other fragmentsmay have the first physical UMI and the third physical UMI; stillfurther fragments may have the third physical UMI and a fourth physicalUMI, and so on. In this example, the source fragment(s) for theseamplified fragments may be difficult to ascertain. Apparently, duringthe amplification process, the physical UMI may have been “swapped out”by another physical UMI.

One possible approach to addressing this UMI jumping problem considersonly fragments sharing both UMIs as deriving from the same sourcemolecule, while fragments sharing only one UMI will be excluded fromanalysis. However, some of these fragments sharing only one physical UMImay indeed derive from the same molecule as those sharing both physicalUMIs. By excluding the fragments sharing just one physical UMI fromconsideration, useful information may be lost. Another possible approachconsiders any fragments having one common physical UMI as deriving fromthe same source molecule. But this approach does not allow combining twophysical UMIs on two ends of the fragments for downstream analysis.Furthermore, under either approach, for the example above, fragmentssharing the first and second physical UMIs would not be considered toderive from the same source molecule as fragments sharing the third andfourth physical UMIs. This may or may not be true. A third approach mayaddress the UMI jumping problem by using adapters with physical UMIs onboth strands of the single-stranded region, such as the adapters in FIG.2A(v)-(vi). The third approach is further explained below following adescription of a hypothetical mechanism underlying UMI jumping.

FIG. 2B illustrates a hypothetical process in which UMI jumping occursin a PCR reaction involving adapters having two physical UMIs on twoarms. The two physical UMIs may be random UMIs or nonrandom UMIs. Theactual underlying mechanism of UMI jumping and the hypothetical processdescribed here do not affect the utility of the adapters and methodsdisclosed herein. The PCR reaction starts by providing at least onedouble stranded source DNA fragment 202 and adapters 204 and 206.Adapters 204 and 206 are similar to the adapters illustrated in FIG.2A(iii)-(iv). Adapter 204 has a P5 adapter sequence and an al physicalUMI on its 5′ arm. Adapter 204 also has a P7′ adapter sequence and an α2physical UMI on its 3′ arm. Adapter 206 has a P5 adapter sequence and aβ2 physical UMI on its 5′ arm, and a P7′ adapter sequence and a β1physical UMI on its 3′ arm. The process proceeds by ligating adapter 204and adapter 206 to fragment 202, obtaining ligation product 208. Theprocess proceeds by denaturing ligation product 208, resulting in asingle stranded, denatured fragment 212. Meanwhile, a reaction mixtureoften includes residual adapters at this stage. Because even if theprocess has already involved removing overabundant adapters such asusing Solid Phase Reversible Immobilization (SPRI) beads, some adaptersare still left over in the reaction mixture. Such a leftover adapter isillustrated as adapter 210, which is similar to adapter 206, except thatadapter 210 has physical UMIs γ1 and γ2 on its 3′ and 7′ arms,respectively. The denaturing condition producing the denatured fragment212 also produces a denatured adapter oligonucleotide 216, which hasphysical UMI γ1 near its P7′ adapter sequence.

The PCR reaction involves priming the denatured fragment 212 with a PCRprimer 214 and extending the primer 214, thereby forming adouble-stranded fragment that is then denatured to form asingle-stranded, intermediate fragment 220 complementary to fragment212. The PCR process also primes the denatured oligonucleotide 216 witha PCR primer 218 and extending the primer 218, thereby forming adouble-stranded fragment that is then denatured to form asingle-stranded, intermediate adapter oligonucleotide 222 complementaryto fragment 212. Before the next cycle of PCR amplification,intermediate adapter oligonucleotides 222 hybridize to intermediatefragment 220 near the P7′ end and downstream of the physical UMI β1. Thehybridized region corresponds to the single-stranded regions of adapter206 and adapter 210, because these single-stranded regions share thesame sequence.

The hybridized product of intermediate fragment 220 and intermediateadapter oligonucleotide 222 provides a template that can then be primedby a P7′ PCR primer 224 at the 5′ end of oligonucleotide 222 andextended. During extension, the extension template switches tointermediate fragment 220 when intermediate adapter oligonucleotide 222ends. The template switching provides a possible mechanism for UMIjumping. After extension and denaturing, a single-stranded fragment 226is produced, which is otherwise complementary to intermediate fragment220 but it has the physical UMI γ1 instead of the physical UMI β1 inintermediate fragment 220. Similarly, single-stranded fragment 226 isthe same as fragment 212 except that it has the physical UMI γ1 insteadof the physical UMI β1.

In some implementations of the disclosure, using adapters havingphysical UMIs on both strands of the double-stranded region of theadapters, such as the adapters in FIG. 2A(v)-(vi), may prevent or reduceUMI jumping. This may be due to the fact that the physical UMIs on oneadapter at the double-stranded region are different from physical UMIson all other adapters. This helps to reduce the complementarity betweenintermediate adapter oligonucleotides and intermediate fragments,thereby avoiding hybridization such as that shown for intermediateoligonucleotide 222 and intermediate fragment 220, thereby reducing orpreventing UMI jumping.

Random Physical UMIs and Nonrandom Physical UMIs

In some implementations of the adapters described above, the physicalUMIs in the adapters include random UMIs. In some implementations, eachrandom UMI is different from every other random UMI applied to DNAfragments. In other words, the random UMIs are randomly selected withoutreplacement from a set of UMIs including all possible different UMIsgiven the sequence length(s). In other implementations, the random UMIsare randomly selected with replacement. In these implementations, twoadapters may have the same UMI due to random chance.

In some implementations, the physical UMIs in the adapters includenonrandom UMIs. In some implementations, multiple adapters include thesame nonrandom UMI sequence. For instance, a set of 96 differentnonrandom UMIs may be applied to 100,000 distinct molecules/fragmentsfrom a sample. In some implementations, each nonrandom UMI of the setdiffers from every other UMI of the set by two nucleotides. In otherwords, each nonrandom UMI requires that it at least two of itsnucleotides be replaced before matching the sequence of any othernonrandom UMI used in the sequencing. In other implementations, eachnonrandom UMI of the set differs from every other UMI of the set bythree or more nucleotides.

FIG. 2C shows a process for making adapters having random UMIs on bothstrands of the adapters in the double-stranded region, where twoadapters on two strands are complimentary to each other. The processstarts by providing a sequencing adapter 230 having a hybridized,double-stranded region and two single-stranded arms. The resultingadapter is similar to that shown in FIG. 2A(v). In the exampleillustrated here, the D7XX sequence corresponds to the i7 index sequencein FIG. 2A(v); the SBS12′ sequence corresponds to the read 1 primersequence in FIG. 2A(v); the D50X corresponds to i5 index sequence inFIG. 2A(v); and the SBS3 corresponds to the read 2 primer sequence inFIG. 2A(v). Sequencing adapter 232 includes a 15-mer over-hangCCANNNNANNNNTGG (SEQ ID NO:1) at the end of the double-strandedhybridized region upstream of the SBS12′ read primer sequence. Theletter N represents random nucleotides, of which the four between A andTGG will be used to provides a physical UMI at the 5′ end of the SBS12′strand. The 15-mer over-hang can be recognized by restriction enzymeXcm1, because Xcm1 recognizes 15-mers having CCA at the 5′ and TGG atthe 3′ end. Process 230 then proceeds to extend the 3′ end of the SPS3strand using the 15-mer as an extension template, thereby producing anextension product 234. Extension product 234 has a tyrosine at themid-point of the 15-mer on the SBS3 strand corresponding to theadenosine on the SBS12′ strand. The tyrosine residue will become theresidue at the 3′ end of the double-stranded region of the adapter endproduct of process 230. The tyrosine residue can hybridize to theadenosine residue at the 3′ A-tail of an insert.

Process 230 proceeds by applying restriction enzyme Xcm1 to digest thenewly extended end of extension product 234. Xcm1 is a restrictionendonuclease that recognizes 15-mers having CCA at the 5′ and TGG at the3′ end, and its phosphodiesterase activity digests a nucleic acid strandby severing the phosphodiester bond between the 8^(th) and 9^(th)nucleotides counting from the CAA 5′ end. This digestion mechanismdigests the double stranded end of extension product 234 immediatelydownstream of the adenosine residue on the SBS12′ strand and downstreamof the tyrosine residue on the SBS3 strand. The digestion results in anadapter 236 that has four random nucleotides the 5′ end of itsdouble-stranded region upstream of the SBS12′ sequence. Adapter 236 alsohas a tyrosine overhang and four random nucleotides at the 3′ end of itsdouble-stranded region downstream of the SBS3 sequence. The four randomnucleotides on each strand provide a physical UMI, and the two physicalUMIs on the two strands are complementary to each other.

FIG. 2D shows a diagram of an adapter having a SBS13 arm top strand (SEQID NO:2) and a SBS3 arm bottom strand (SEQ ID NO:3), illustrating thenucleotides in the adapter. The adapter is similar to adapter 236 inFIG. 2C, but it has four base pairs between the recognition site of Xcm1and the read sequences of the adapter. Also, the adapter shown in FIG.2D is a shortened version of adapter 236 that eliminates the P7/P5 andindex sequence in the adapter, which increases adapter stability. On thetop strand of the adapter (SEQ ID NO:2) in the double-stranded region,starting from the 5′ end, the adapter has four random nucleotides for aphysical UMI, followed by TGG as the recognition site for restrictionenzyme Xcm1, followed by TCGC upstream of the read sequence. The TCGCnucleotides are incorporated to provide stability to the adapter. Theyare optional in some implementations.

Nucleotides may be added to provide stability in adapter production,sample preparation and processing. It has been observed that theannealing efficiency of the top and bottom oligos to create the initialadapter template is enhanced upon providing additional TCGC bases evenin room temperature. Because the Klenow extension and Xcm1 digestionduring adapter production is performed at higher temperatures (30° C.and 37° C., respectively), the additional of TCGC may enhance adapterstability. It is possible to use different sequences or varyingnucleotide lengths besides TCGC to improve adapter stability.

In some implementations, additional sequences other than stabilizingsequences may be incorporated into the adapter for other purposeswithout affecting the adapter's function to provide unique indices toDNA fragments. The bottom strand of the adapter (SEQ ID NO:3) in thedouble-stranded region is complementary to the top strand, except thatit includes a T overhang at the 3′ end. The four random nucleotides atthe bottom strand provide a second physical UMI.

Random UMIs such as the ones illustrated in FIGS. 2C and 2D provide alarger number of unique UMIs than nonrandom UMIs of the same sequencelength. In other words, random UMIs are more likely to be unique thannonrandom UMIs. However, in some implementations, nonrandom UMIs may beeasier to manufacture or have higher conversion efficiency. Whennonrandom UMIs are combined with other information such as sequenceposition and virtual UMI, they can provide an efficient mechanism toindex the source molecules of DNA fragments.

In various implementations, nonrandom UMIs are identified taking intoconsideration's various factors, including but not limited to, means fordetecting errors within the UMI sequences, conversion efficiency, assaycompatibility, GC content, homopolymers, and manufacturingconsiderations.

For instance, nonrandom UMIs may be designed to provide a mechanism forfacilitating error detection. FIG. 2E schematically illustrates anonrandom UMI design that provides a mechanism for detecting errors thatoccur in the UMI sequence during a sequencing process. According to thisdesign, each of the nonrandom UMIs has six nucleotides and differs fromevery other UMI by at least two nucleotides. As illustrated in FIG. 2E,the nonrandom UMI 244 differs from the nonrandom UMI 242 in the firsttwo nucleotides from the left, as shown by the underlined nucleotides Tand G in UMI 244 and nucleotides A and C in UMI 242. UMI 246 is asequence identified as part of a read, and it is different from allother UMIs of adapters provided in the process. Since the UMI sequencein a read is supposedly derived from a UMI in an adapter, an errorlikely has occurred during the sequencing process, such as duringamplification or sequencing. UMI 242 and UMI 244 are illustrated as thetwo UMIs most similar to the UMI 246 in the read. It can be seen thatUMI 246 differs from UMI 242 by one nucleotide in the first nucleotidefrom the left, which is T instead of A. Moreover, UMI 246 also differsfrom UMI 244 by one nucleotide, albeit in the second nucleotide from theleft, which is C instead of G. Because UMI 246 in the read differs fromboth UMI 242 and UMI 244 by one nucleotide, from the informationillustrated, it cannot be determined whether UMI 246 is derived from UMI242 or UMI 244. However, in many other scenarios, the UMI errors in thereads are not equally different from the two most similar UMIs. As shownin the example for UMI 248, UMI 242 and UMI 244 are also the two UMIsmost similar to the UMI 248. It can be seen that UMI 248 differs fromUMI 242 by one nucleotide in the third nucleotide from the left, whichis A instead of T. In contrast, UMI 248 differs from UMI 244 by threenucleotides. Therefore, it cannot be determined UMI 248 is derived fromUMI 242 instead of UMI 244, and an error likely occurred in the thirdnucleotide from the left.

Virtual UMIs

Turning to virtual UMI, those Virtual UMIs that are defined at, or withrespect to, the end positions of source DNA molecules can uniquely ornearly uniquely define individual source DNA molecules when thelocations of the end positions are generally random as with somefragmentation procedures and with naturally occurring cfDNA. When thesample contains relatively few source DNA molecules, the virtual UMIscan themselves uniquely identify individual source DNA molecules. Usinga combination of two virtual UMIs, each associated with a different endof a source DNA molecule, increases the likelihood that virtual UMIsalone can uniquely identify source DNA molecules. Of course, even insituations where one or two virtual UMIs cannot alone uniquely identifysource DNA molecules, the combination of such virtual UMIs with one ormore physical UMIs may succeed.

If two reads are derived from the same DNA fragment, two subsequenceshaving the same base pairs will also have the same relative location inthe reads. On the contrary, if two reads are derived from two differentDNA fragments, it is unlikely that two subsequences having the same basepairs have the exact same relative location in the reads. Therefore, iftwo or more subsequences from two or more reads have the same base pairsand the same relative location on the two or more reads, it can beinferred that the two or more reads are derived from the same fragment.

In some implementations, subsequences at or near the ends of a DNAfragment are used as virtual UMIs. This design choice has some practicaladvantages. First, the relative locations of these subsequences on thereads are easily ascertained, as they are at or near the beginning ofthe reads and the system need not use an offset to find the virtual UMI.Furthermore, since the base pairs at the ends of the fragments are firstsequenced, those base pairs are available even if the reads arerelatively short. Moreover, base pairs determined earlier in a long readhave lower sequencing error rate than those determined later. In otherimplementations, however, subsequences located away from the ends of thereads can be used as virtual UMIs, but their relative positions on thereads may need to be ascertained to infer that the reads are obtainedfrom the same fragment.

One or more subsequences in a read may be used as virtual UMIs. In someimplementations, two subsequences, each tracked from a different end ofthe source DNA molecule, are used as virtual UMIs. In variousimplementations, virtual UMIs are about 24 base pairs or shorter, about20 base pairs or shorter, about 15 base pairs or shorter, about 10 basepairs or shorter, about 9 base pairs or shorter, about 8 base pairs orshorter, about 7 base pairs or shorter, or about 6 base pairs orshorter. In some implementations, virtual UMIs are about 6 to 10 basepairs. In other implementations, virtual UMIs are about 6 to 24 basepairs.

Collapsing Reads and Obtaining Consensus Sequences

In various implementations using UMIs, multiple sequence reads havingthe same UMI(s) are collapsed to obtain one or more consensus sequences,which are then used to determine the sequence of a source DNA molecule.Multiple distinct reads may be generated from distinct instances of thesame source DNA molecule, and these reads may be compared to produce aconsensus sequence as described herein. The instances may be generatedby amplifying a source DNA molecule prior to sequencing, such thatdistinct sequencing operations are performed on distinct amplificationproducts, each sharing the source DNA molecule's sequence. Of course,amplification may introduce errors such that the sequences of thedistinct amplification products have differences. In the context somesequencing technologies such as Illumina's sequencing-by-synthesis, asource DNA molecule or an amplification product thereof forms a clusterof DNA molecules linked to a region of a flow cell. The molecules of thecluster collectively provide a read. Typically, at least two reads arerequired to provide a consensus sequence. Sequencing depths of 100,1000, and 10,000 are examples of sequencing depths useful in thedisclosed embodiments for creating consensus reads for low allelefrequencies (e.g., about 1% or less).

In some implementations, nucleotides that are consistent across 100% ofthe reads sharing a UMI or combination of UMIs are included in theconsensus sequence. In other implementations, consensus criterion can belower than 100%. For instance, a 90% consensus criterion may be used,which means that base pairs that exist in 90% or more of the reads inthe group are included in the consensus sequence. In variousimplementations, the consensus criterion may be set at about 30%, about40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%,or about 100%.

Collapsing by Physical UMIs and Virtual UMIs

Multiple techniques may be used to collapse reads that include multipleUMIs. In some implementations, reads sharing a common physical UMI maybe collapsed to obtain a consensus sequence. In some implementations, ifthe common physical UMI is a random UMI, the random UMI may be uniqueenough to identify a particular source molecule of a DNA fragment in asample. In other implementations, if the common physical UMI is anonrandom UMI, the UMI may not be unique enough by itself to identify aparticular source molecule. In either case, a physical UMI may becombined with a virtual UMI to provide an index of the source molecule.

In the example workflow described above and depicted in FIGS. 1B, 3A,and 4, some reads include α-ρ-φ UMIs, while others include β-φ-ρUMIs.The physical UMI α produces reads having α. If all adapters used in aworkflow have different physical UMIs (e.g., different random UMIs), allreads having a at the adapter region are likely derived from the samestrand of the DNA fragment. Similarly the physical UMI β produces readshaving β, all of which are derived from the same complementary strand ofthe DNA fragment. It is therefore useful to collapse all reads includinga to obtain one consensus sequence, and to collapse all reads includingβ to obtain another consensus sequence. This is illustrated as the firstlevel collapsing in FIGS. 4B-4C. Because all reads in a group arederived from the same source polynucleotide in a sample, base pairsincluded in the consensus sequence likely reflect the true sequence ofthe source polynucleotide, while a base pair excluded from the consensussequence likely reflects a variation or error introduced in theworkflow.

In addition, the virtual UMIs ρ and φ can provide information todetermine that reads including one or both virtual UMIs are derived fromthe same source DNA fragment. Because virtual UMIs ρ and φ are internalto the source DNA fragments, the exploitation of the virtual UMIs do notadd overhead to preparation or sequencing in practice. After obtainingthe sequences of the physical UMIs from reads, one or more sub-sequencesin the reads may be determined as virtual UMIs. If the virtual UMIsinclude sufficient base pairs and have the same relative location onreads, they may uniquely identify the reads as having been derived fromthe source DNA fragment. Therefore, reads having one or both virtualUMIs ρ and φ may be collapsed to obtain a consensus sequence. Thecombination of virtual UMIs and physical UMIs can provide information toguide a second-level collapsing when only one physical UMI is assignedto a first level consensus sequence of each strand, such as shown inFIG. 3A and FIGS. 4A-4C. However, in some implementations, this secondlevel collapsing using virtual UMIs may be difficult if there areover-abundant input DNA molecules or fragmentation is not randomized.

In alternative embodiments, reads having two physical UMIs on both ends,such as those shown in FIG. 3B and FIGS. 4D and 4E, may be collapsed ina second-level collapsing based on a combination of the physical UMIsand the virtual UMIs. This is especially helpful when the physical UMIsare too short to uniquely identify source DNA fragments without usingthe virtual UMIs. In these embodiments, second level collapsing can beimplemented, with physical duplex UMIs as shown in FIG. 3B, bycollapsing α-ρ-φ-β consensus reads and β-φ-ρ-α consensus reads from thesame DNA molecule, thereby obtaining a consensus sequence includingnucleotides consistent among all of the reads.

Using UMI and collapsing scheme described herein, various embodimentscan suppress different sources of error affecting the determinedsequence of a fragment even if the fragment includes alleles with verylow allele frequencies. Reads sharing the same UMIs (physical and/orvirtual) are grouped together. By collapsing the grouped reads, variants(SNV and small indels) due to PCR, library preparation, clustering, andsequencing errors can be eliminated. FIGS. 4A-4E illustrate how a methodas disclosed in an example workflow can suppress different sources oferror in determining the sequence of a double stranded DNA fragment. Theillustrated reads include α-ρ-φ or β-φ-ρUMIs in FIGS. 3A and 4A-4C, andα-ρ-φ-β or β-φ-ρ-α UMIs in FIGS. 3B, 4D and 4E. The α and β UMIs aresingleplex physical UMIs in FIGS. 3A and 4A-4C. The α and β UMIs areduplex UMIs in FIGS. 3B, 4D and 4E. The virtual UMIs ρ and φ are locatedat the ends of a DNA fragment.

The method using singleplex physical UMIs as shown in FIGS. 4A-4C firstinvolves collapsing reads having the same physical UMI α or β,illustrated as first level collapsing. The first level collapsingobtains an α consensus sequence for reads having the physical UMI α,which reads are derived from one strand of the double-stranded fragment.The first level collapsing also obtains a β consensus sequence for readshaving the physical UMI β, which reads are derived from another strandof the double-stranded fragment. At a second level collapsing, themethod obtains a third consensus sequence from the a consensus sequenceand the β consensus sequence. The third consensus sequence reflectsconsensus base pairs from reads having the same duplex virtual UMIs ρand φ, which reads are derived from two complementary strands of thesource fragment. Finally, the sequence of the double stranded DNAfragment is determined as the third consensus sequence.

The method using duplex physical UMIs as shown in FIGS. 4D-4E firstinvolves collapsing reads having the physical UMIs α and β with an α→βorder in the 5′-3′ direction, illustrated as first level collapsing. Thefirst level collapsing obtains an α-β consensus sequence for readshaving the physical UMIs α and β, which reads are derived from a firststrand of the double-stranded fragment. The first level collapsing alsoobtains a β-α consensus sequence for reads having the physical UMIs βand α with a β→α order in the 5′-3′ direction, which reads are derivedfrom a second strand complementary to the first strand of thedouble-stranded fragment. At a second level collapsing, the methodobtains a third consensus sequence from the α-β consensus sequence andthe β-α consensus sequence. The third consensus sequence reflectsconsensus base pairs from reads having the same duplex virtual UMIs ρand φ, which reads are derived from two strands of the fragment.Finally, the sequence of the double stranded DNA fragment is determinedas the third consensus sequence.

FIG. 4A illustrates how a first-level collapsing may suppress sequencingerrors. Sequencing errors occur on the sequencing platform after sampleand library preparation (e.g., PCR amplification). Sequencing errors mayintroduce different erroneous bases into different reads. True positivebases are illustrated by solid letters, while false positive bases areillustrated by hatched letters. False positive nucleotides on differentreads in the α-ρ-φ family have been excluded from the a consensussequence. The true positive nucleotide “A” illustrated on the left endsof the α-ρ-φ family reads is retained for the a consensus sequence.Similarly, false positive nucleotides on different reads in the β-φ-ρfamily have been excluded from the β consensus sequence, retaining thetrue positive nucleotide “A”. As illustrated here, the first levelcollapsing can effectively remove sequencing errors. FIG. 4A also showsan optional second-level collapsing relying on the virtual UMIs ρ and φ.This second-level collapsing may further suppress errors as explainedabove, but such errors are not illustrated in FIG. 4A.

PCR errors occur before clustering amplification. Therefore, oneerroneous base pair introduced into a single stranded DNA by the PCRprocess may be amplified during clustering amplification, therebyappearing in multiple clusters and reads. As illustrated in FIG. 4B andFIG. 4D, a false positive base pair introduced by PCR error may appearin many reads. The “T” base in the α-ρ-φ (FIG. 4B) or α-β (FIG. 4D)family reads and the “C” base in the β-φ-ρ (FIG. 4B) or β-α (FIG. 4D)family reads are such PCR errors. In contrast, the sequencing errorsshown in FIG. 4A appear on one or a few reads in the same family.Because PCR sequencing errors appear in many reads of the family, afirst-level collapsing of reads in a strand does not remove the PCRerrors, even though the first-level collapsing removes sequencing errors(e.g., G and A removed from the α-ρ-φ family in FIG. 4B and the α-βfamily in FIG. 4D). However, since a PCR error is introduced into asingle stranded DNA, the complementary strand of the source fragment andreads derived therefrom usually do not have the same PCR error.Therefore, the second-level collapsing based on reads from the twostrands of the source fragment can effectively remove PCR errors asshown at the bottom of FIGS. 4B and 4D.

In some sequencing platforms, homopolymer errors occur to introducesmall indel errors into homopolymers of repeating single nucleotides.FIGS. 4C and 4E illustrate homopolymer error correction using themethods described herein. In the α-ρ-φ (FIG. 4C) or α-ρ-φ-β (FIG. 4E)family reads, two “T” nucleotides have been deleted from the second readfrom the top, and one “T” nucleotide has been deleted from the thirdread from the top. In the β-φ-ρ (FIG. 4C) or β-φ-ρ-α (FIG. 4E) familyreads, one “A” nucleotides has been inserted into the first read fromthe top. Similar to sequencing error illustrated in FIG. 4A, homopolymererrors occur after PCR amplification, therefore different reads havedifferent homopolymer errors. As a result, the first level collapsingcan effectively remove indel errors.

Consensus sequences may be obtained by collapsing reads having one ormore common nonrandom UMI and one or more common virtual UMIs.Furthermore, position information may also be used to obtained consensussequences as described below.

Collapsing by Position

In some implementations, reads are processed to align to a referencesequence to determine alignment locations of the reads on the referencesequence (localization). However, in some implementations notillustrated above, localization is achieved by k-mer similarity analysisand read-read alignment. This second implementation has two advantages:first, it can collapse (error correct) reads that do not match thereference, due to haplotype differences or translocations, and secondly,it does not depend on an aligner algorithm, thereby removing thepossibility of aligner-induced artifacts (errors in the aligner). Insome implementations, reads sharing the same localization informationmay be collapsed to obtain consensus sequences to determine the sequenceof the source DNA fragments. In some contexts, the alignment process isalso referred to as a mapping process. Sequence reads undergo analignment process to be mapped to a reference sequence. Variousalignment tools and algorithms may be used to align reads to thereference sequence as described elsewhere in the disclosure. As usual,in alignment algorithms, some reads are successfully aligned to thereference sequence, while others may not be successfully aligned or maybe poorly aligned to the reference sequence. Reads that are successivelyaligned to the reference sequence are associated with sites on thereference sequence. Aligned reads and their associated sites are alsoreferred to as sequence tags. Some sequence reads that contain a largenumber of repeats tend to be harder to align to the reference sequence.When a read is aligned to a reference sequence with a number ofmismatched bases above a certain criterion, the read is consideredpoorly aligned. In various embodiments, reads are considered poorlyaligned when they are aligned with at least about 1, 2, 3, 4, 5, 6, 7,8, 9, or 10 mismatches. In other embodiments, reads are consideredpoorly aligned when they are aligned with at least about 5% ofmismatches. In other embodiments, reads are considered poorly alignedwhen is they are aligned with at least about 10%, 15%, or 20% mismatchedbases.

In some implementations, the disclosed methods combine positioninformation with physical UMI information to index source molecules ofDNA fragments. Sequence reads sharing a same read position and a samenonrandom or random physical UMI may be collapsed to obtain a consensussequence for determining the sequence of a fragment or portion thereof.In some implementations, sequence reads sharing the same read position,the same nonrandom physical UMI, and a random physical UMI may becollapsed to obtain a consensus sequence. In such implementations, theadapter may include both a nonrandom physical UMI and a random physicalUMI. In some implementations, sequence reads sharing the same readposition and the same virtual UMI may be collapsed to obtain a consensussequence.

Read position information may be obtained by different techniques. Forexample, in some implementations, genomic coordinates may be used toprovide read position information. In some implementations, the positionon a reference sequence to which a read is aligned can be used toprovide read position information. For example, the start and stoppositions of a read on a chromosome may be used to provide read positioninformation. In some implementations, read positions are considered thesame if they have identical position information. In someimplementations, read positions are considered the same if thedifference between the position information is smaller than a definedcriterion. For instance, two reads having start genomic positions thatdiffer by less than 2, 3, 4, or 5, base pairs can be considered as readshaving the same read position. In other implementations, read positionsare considered the same if their position information can be convertedto and matched in a particular position space. A reference sequence maybe provided prior to sequencing—for example, it may be a well-known andwidely-used human genomic sequence—or it may be determined from thereads obtained during sequencing the sample.

Regardless of the specific sequencing platform and protocol, at least aportion of the nucleic acids contained in the sample are sequenced togenerate tens of thousands, hundreds of thousands, or millions ofsequence reads, e.g., 100 bp reads. In some embodiments, the sequencereads include about 20 bp, about 25 bp, about 30 bp, about 35 bp, about36 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp,about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130,about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp,about 350 bp, about 400 bp, about 450 bp, about 500 bp, about 800 bp,about 1000 bp, or about 2000 bp.

In some embodiments, reads are aligned to a reference genome, e.g.,hg19. In other embodiments, reads are aligned to a portion of areference genome, e.g., a chromosome or a chromosome segment. The readsthat are uniquely mapped to the reference genome are known as sequencetags. In one embodiment, at least about 3×10⁶ qualified sequence tags,at least about 5×10⁶ qualified sequence tags, at least about 8×10⁶qualified sequence tags, at least about 10×10⁶ qualified sequence tags,at least about 15×10⁶ qualified sequence tags, at least about 20×10⁶qualified sequence tags, at least about 30×10⁶ qualified sequence tags,at least about 40×10⁶ qualified sequence tags, or at least about 50×10⁶qualified sequence tags are obtained from reads that map uniquely to areference genome.

Applications

In various applications, error correction strategies as disclosed hereinmay provide one or more of the following benefits: (i) detect very lowallele frequency somatic mutations, (ii) decrease cycle time bymitigating phasing/prephasing errors, and/or (iii) increase read lengthby boosting quality of base calls at the later part of reads, etc. Theapplications and rationales regarding detection of low allele frequencysomatic mutations are discussed above.

In certain embodiments, the techniques described herein may permitreliable calling of alleles having frequencies of about 2% or less, orabout 1% or less, or about 0.5% or less. Such low frequencies are commonin cfDNA originating from tumor cells in a cancer patient. In someembodiments, the techniques described here may permit the identificationof rare strains in metagenomic samples, as well as the detection of rarevariants in viral or other populations when, for example, a patient hasbeen infected by multiple viral strains, and/or has undergone medicaltreatment.

In certain embodiments, the techniques described herein may allowshorter sequencing chemistry cycle time. The shortened cycle timeincreases sequencing errors, which can be corrected using methoddescribed above.

In some implementations involving UMIs, long reads may be obtained frompaired end sequencing using asymmetric read lengths for a pair ofpaired-end (PE) reads from two ends of a segment. For instance, a pairof reads having 50 bp in one paired-end read and 500 bp in anotherpaired-end read can be may be “stitched” together with another pair ofreads to produce a long read of 1000 bp. These implementations mayprovide faster sequencing speed for to determine long fragments of lowallele frequencies.

FIG. 5 schematically illustrates an example to efficiently obtain longpaired end reads in this kind of applications by applying physical UMIsand virtual UMIs. Libraries from both strands of same DNA fragments areclustered on the flowcell. The insert size of library is longer than 1Kb. Sequencing is performed with asymmetric read lengths (e.g.,Read1=500 bp, Read2=50 bp), to ensure the quality of long 500 bp reads.Stitching two strands, 1000 bp long PE reads can be created with only500+50 bp sequencing.

Samples

Samples that are used for determining DNA fragment sequence can includesamples taken from any cell, fluid, tissue, or organ including nucleicacids in which sequences of interest are to be determined. In someembodiments involving diagnosis of cancers, circulating tumor DNA may beobtained from a subject's bodily fluid, e.g. blood or plasma. In someembodiments involving diagnosis of fetus, it is advantageous to obtaincell-free nucleic acids, e.g., cell-free DNA (cfDNA), from maternal bodyfluid. Cell-free nucleic acids, including cell-free DNA, can be obtainedby various methods known in the art from biological samples includingbut not limited to plasma, serum, and urine (see, e.g., Fan et al., ProcNatl Acad Sci 105:16266-16271 [2008]; Koide et al., Prenatal Diagnosis25:604-607 [2005]; Chen et al., Nature Med. 2: 1033-1035 [1996]; Lo etal., Lancet 350: 485-487 [1997]; Botezatu et al., Clin Chem. 46:1078-1084, 2000; and Su et al., J Mol. Diagn. 6: 101-107 [2004]).

In various embodiments the nucleic acids (e.g., DNA or RNA) present inthe sample can be enriched specifically or non-specifically prior to use(e.g., prior to preparing a sequencing library). Non-specific enrichmentof sample DNA refers to the whole genome amplification of the genomicDNA fragments of the sample that can be used to increase the level ofthe sample DNA prior to preparing a cfDNA sequencing library. Methodsfor whole genome amplification are known in the art. Degenerateoligonucleotide-primed PCR (DOP), primer extension PCR technique (PEP)and multiple displacement amplification (MDA) are examples of wholegenome amplification methods. In some embodiments, the sample isun-enriched for DNA.

The sample including the nucleic acids to which the methods describedherein are applied typically include a biological sample (“test sample”)as described above. In some embodiments, the nucleic acids to besequenced are purified or isolated by any of a number of well-knownmethods.

Accordingly, in certain embodiments the sample includes or consistsessentially of a purified or isolated polynucleotide, or it can includesamples such as a tissue sample, a biological fluid sample, a cellsample, and the like. Suitable biological fluid samples include, but arenot limited to blood, plasma, serum, sweat, tears, sputum, urine,sputum, ear flow, lymph, saliva, cerebrospinal fluid, ravages, bonemarrow suspension, vaginal flow, trans-cervical lavage, brain fluid,ascites, milk, secretions of the respiratory, intestinal andgenitourinary tracts, amniotic fluid, milk, and leukophoresis samples.In some embodiments, the sample is a sample that is easily obtainable bynon-invasive procedures, e.g., blood, plasma, serum, sweat, tears,sputum, urine, stool, sputum, ear flow, saliva or feces. In certainembodiments the sample is a peripheral blood sample, or the plasmaand/or serum fractions of a peripheral blood sample. In otherembodiments, the biological sample is a swab or smear, a biopsyspecimen, or a cell culture. In another embodiment, the sample is amixture of two or more biological samples, e.g., a biological sample caninclude two or more of a biological fluid sample, a tissue sample, and acell culture sample. As used herein, the terms “blood,” “plasma” and“serum” expressly encompass fractions or processed portions thereof.Similarly, where a sample is taken from a biopsy, swab, smear, etc., the“sample” expressly encompasses a processed fraction or portion derivedfrom the biopsy, swab, smear, etc.

In certain embodiments, samples can be obtained from sources, including,but not limited to, samples from different individuals, samples fromdifferent developmental stages of the same or different individuals,samples from different diseased individuals (e.g., individuals suspectedof having a genetic disorder), normal individuals, samples obtained atdifferent stages of a disease in an individual, samples obtained from anindividual subjected to different treatments for a disease, samples fromindividuals subjected to different environmental factors, samples fromindividuals with predisposition to a pathology, samples individuals withexposure to an infectious disease agent, and the like.

In one illustrative, but non-limiting embodiment, the sample is amaternal sample that is obtained from a pregnant female, for example apregnant woman. In this instance, the sample can be analyzed using themethods described herein to provide a prenatal diagnosis of potentialchromosomal abnormalities in the fetus. The maternal sample can be atissue sample, a biological fluid sample, or a cell sample. A biologicalfluid includes, as non-limiting examples, blood, plasma, serum, sweat,tears, sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinalfluid, ravages, bone marrow suspension, vaginal flow, transcervicallavage, brain fluid, ascites, milk, secretions of the respiratory,intestinal and genitourinary tracts, and leukophoresis samples.

In certain embodiments samples can also be obtained from in vitrocultured tissues, cells, or other polynucleotide-containing sources. Thecultured samples can be taken from sources including, but not limitedto, cultures (e.g., tissue or cells) maintained in different media andconditions (e.g., pH, pressure, or temperature), cultures (e.g., tissueor cells) maintained for different periods of length, cultures (e.g.,tissue or cells) treated with different factors or reagents (e.g., adrug candidate, or a modulator), or cultures of different types oftissue and/or cells.

Methods of isolating nucleic acids from biological sources are wellknown and will differ depending upon the nature of the source. One ofskill in the art can readily isolate nucleic acids from a source asneeded for the method described herein. In some instances, it can beadvantageous to fragment the nucleic acid molecules in the nucleic acidsample. Fragmentation can be random, or it can be specific, as achieved,for example, using restriction endonuclease digestion. Methods forrandom fragmentation are well known in the art, and include, forexample, limited DNAse digestion, alkali treatment and physicalshearing.

Sequencing Library Preparation

In various embodiments, sequencing may be performed on varioussequencing platforms that require preparation of a sequencing library.The preparation typically involves fragmenting the DNA (sonication,nebulization or shearing), followed by DNA repair and end polishing(blunt end or A overhang), and platform-specific adapter ligation. Inone embodiment, the methods described herein can utilize next generationsequencing technologies (NGS), that allow multiple samples to besequenced individually as genomic molecules (i.e., singleplexsequencing) or as pooled samples including indexed genomic molecules(e.g., multiplex sequencing) on a single sequencing run. These methodscan generate up to several billion reads of DNA sequences. In variousembodiments the sequences of genomic nucleic acids, and/or of indexedgenomic nucleic acids can be determined using, for example, the NextGeneration Sequencing Technologies (NGS) described herein. In variousembodiments analysis of the massive amount of sequence data obtainedusing NGS can be performed using one or more processors as describedherein.

In various embodiments the use of such sequencing technologies does notinvolve the preparation of sequencing libraries.

However, in certain embodiments the sequencing methods contemplatedherein involve the preparation of sequencing libraries. In oneillustrative approach, sequencing library preparation involves theproduction of a random collection of adapter-modified DNA fragments(e.g., polynucleotides) that are ready to be sequenced. Sequencinglibraries of polynucleotides can be prepared from DNA or RNA, includingequivalents, analogs of either DNA or cDNA, for example, DNA or cDNAthat is complementary or copy DNA produced from an RNA template, by theaction of reverse transcriptase. The polynucleotides may originate indouble-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA,PCR amplification products, and the like) or, in certain embodiments,the polynucleotides may originated in single-stranded form (e.g., ssDNA,RNA, etc.) and have been converted to dsDNA form. By way ofillustration, in certain embodiments, single stranded mRNA molecules maybe copied into double-stranded cDNAs suitable for use in preparing asequencing library. The precise sequence of the primary polynucleotidemolecules is generally not material to the method of librarypreparation, and may be known or unknown. In one embodiment, thepolynucleotide molecules are DNA molecules. More particularly, incertain embodiments, the polynucleotide molecules represent the entiregenetic complement of an organism or substantially the entire geneticcomplement of an organism, and are genomic DNA molecules (e.g., cellularDNA, cell free DNA (cfDNA), etc.), that typically include both intronsequence and exon sequence (coding sequence), as well as non-codingregulatory sequences such as promoter and enhancer sequences. In certainembodiments, the primary polynucleotide molecules include human genomicDNA molecules, e.g., cfDNA molecules present in peripheral blood of apregnant subject.

Preparation of sequencing libraries for some NGS sequencing platforms isfacilitated by the use of polynucleotides including a specific range offragment sizes. Preparation of such libraries typically involves thefragmentation of large polynucleotides (e.g. cellular genomic DNA) toobtain polynucleotides in the desired size range.

Paired end reads may be used for the sequencing methods and systemsdisclosed herein. The fragment or insert length is longer than the readlength, and sometimes longer than the sum of the lengths of the tworeads.

In some illustrative embodiments, the sample nucleic acid(s) areobtained as genomic DNA, which is subjected to fragmentation intofragments of longer than approximately 50, 100, 200, 300, 400, 500, 600,700, 800, 900, 1000, 2000, or 5000 base pairs, to which NGS methods canbe readily applied. In some embodiments, the paired end reads areobtained from inserts of about 100-5000 bp. In some embodiments, theinserts are about 100-1000 bp long. These are sometimes implemented asregular short-insert paired end reads. In some embodiments, the insertsare about 1000-5000 bp long. These are sometimes implemented aslong-insert mate paired reads as described above.

In some implementations, long inserts are designed for evaluating verylong sequences. In some implementations, mate pair reads may be appliedto obtain reads that are spaced apart by thousands of base pairs. Inthese implementations, inserts or fragments range from hundreds tothousands of base pairs, with two biotin junction adapters on the twoends of an insert. Then the biotin junction adapters join the two endsof the insert to form a circularized molecule, which is then furtherfragmented. A sub-fragment including the biotin junction adapters andthe two ends of the original insert is selected for sequencing on aplatform that is designed to sequence shorter fragments.

Fragmentation can be achieved by any of a number of methods known tothose of skill in the art. For example, fragmentation can be achieved bymechanical means including, but not limited to nebulization, sonicationand hydroshear. However mechanical fragmentation typically cleaves theDNA backbone at C—O, P—O and C—C bonds resulting in a heterogeneous mixof blunt and 3′- and 5′-overhanging ends with broken C—O, P—O and/C—Cbonds (see, e.g., Alnemri and Liwack, J Biol. Chem 265:17323-17333[1990]; Richards and Boyer, J Mol Biol 11:327-240 [1965]) which may needto be repaired as they may lack the requisite 5′-phosphate for thesubsequent enzymatic reactions, e.g., ligation of sequencing adapters,that are required for preparing DNA for sequencing.

In contrast, cfDNA, typically exists as fragments of less than about 300base pairs and consequently, fragmentation is not typically necessaryfor generating a sequencing library using cfDNA samples.

Typically, whether polynucleotides are forcibly fragmented (e.g.,fragmented in vitro), or naturally exist as fragments, they areconverted to blunt-ended DNA having 5′-phosphates and 3′-hydroxyl.Standard protocols, e.g., protocols for sequencing using, for example,the Illumina platform as described in the example workflow above withreference to FIGS. 1A and 1B, instruct users to end-repair sample DNA,to purify the end-repaired products prior to adenylating or dA-tailingthe 3′ ends, and to purify the dA-tailing products prior to theadapter-ligating steps of the library preparation.

Various embodiments of methods of sequence library preparation describedherein obviate the need to perform one or more of the steps typicallymandated by standard protocols to obtain a modified DNA product that canbe sequenced by NGS. An abbreviated method (ABB method), a 1-stepmethod, and a 2-step method are examples of methods for preparation of asequencing library, which can be found in patent application Ser. No.13/555,037 filed on Jul. 20, 2012, which is incorporated by reference byits entirety.

Sequencing Methods

The methods and apparatus described herein may employ next generationsequencing technology (NGS), which allows massively parallel sequencing.In certain embodiments, clonally amplified DNA templates or single DNAmolecules are sequenced in a massively parallel fashion within a flowcell (e.g., as described in Volkerding et al. Clin Chem 55:641-658[2009]; Metzker M Nature Rev 11:31-46 [2010]). The sequencingtechnologies of NGS include but are not limited to pyrosequencing,sequencing-by-synthesis with reversible dye terminators, sequencing byoligonucleotide probe ligation, and ion semiconductor sequencing. DNAfrom individual samples can be sequenced individually (i.e., singleplexsequencing) or DNA from multiple samples can be pooled and sequenced asindexed genomic molecules (i.e., multiplex sequencing) on a singlesequencing run, to generate up to several hundred million reads of DNAsequences. Examples of sequencing technologies that can be used toobtain the sequence information according to the present method arefurther described here.

Some sequencing technologies are available commercially, such as thesequencing-by-hybridization platform from Affymetrix Inc. (Sunnyvale,Calif.) and the sequencing-by-synthesis platforms from 454 Life Sciences(Bradford, Conn.), Illumina/Solexa (Hayward, Calif.) and HelicosBiosciences (Cambridge, Mass.), and the sequencing-by-ligation platformfrom Applied Biosystems (Foster City, Calif.), as described below. Inaddition to the single molecule sequencing performed usingsequencing-by-synthesis of Helicos Biosciences, other single moleculesequencing technologies include, but are not limited to, the SMRT™technology of Pacific Biosciences, the ION TORRENT™ technology, andnanopore sequencing developed for example, by Oxford NanoporeTechnologies.

While the automated Sanger method is considered as a ‘first generation’technology, Sanger sequencing including the automated Sanger sequencing,can also be employed in the methods described herein. Additionalsuitable sequencing methods include, but are not limited to nucleic acidimaging technologies, e.g., atomic force microscopy (AFM) ortransmission electron microscopy (TEM). Illustrative sequencingtechnologies are described in greater detail below.

In some embodiments, the disclosed methods involve obtaining sequenceinformation for the nucleic acids in the test sample by massivelyparallel sequencing of millions of DNA fragments using Illumina'ssequencing-by-synthesis and reversible terminator-based sequencingchemistry (e.g. as described in Bentley et al., Nature 6:53-59 [2009]).Template DNA can be genomic DNA, e.g., cellular DNA or cfDNA. In someembodiments, genomic DNA from isolated cells is used as the template,and it is fragmented into lengths of several hundred base pairs. Inother embodiments, cfDNA or circulating tumor DNA (ctDNA) is used as thetemplate, and fragmentation is not required as cfDNA or ctDNA exists asshort fragments. For example fetal cfDNA circulates in the bloodstreamas fragments approximately 170 base pairs (bp) in length (Fan et al.,Clin Chem 56:1279-1286 [2010]), and no fragmentation of the DNA isrequired prior to sequencing. Illumina's sequencing technology relies onthe attachment of fragmented genomic DNA to a planar, opticallytransparent surface on which oligonucleotide anchors are bound. TemplateDNA is end-repaired to generate 5′-phosphorylated blunt ends, and thepolymerase activity of Klenow fragment is used to add a single A base tothe 3′ end of the blunt phosphorylated DNA fragments. This additionprepares the DNA fragments for ligation to oligonucleotide adapters,which have an overhang of a single T base at their 3′ end to increaseligation efficiency. The adapter oligonucleotides are complementary tothe flow-cell anchor oligos. Under limiting-dilution conditions,adapter-modified, single-stranded template DNA is added to the flow celland immobilized by hybridization to the anchor oligos. Attached DNAfragments are extended and bridge amplified to create an ultra-highdensity sequencing flow cell with hundreds of millions of clusters, eachcontaining about 1,000 copies of the same template. In one embodiment,the randomly fragmented genomic DNA is amplified using PCR before it issubjected to cluster amplification. Alternatively, an amplification-freegenomic library preparation is used, and the randomly fragmented genomicDNA is enriched using the cluster amplification alone (Kozarewa et al.,Nature Methods 6:291-295 [2009]). In some applications, the templatesare sequenced using a robust four-color DNA sequencing-by-synthesistechnology that employs reversible terminators with removablefluorescent dyes. High-sensitivity fluorescence detection is achievedusing laser excitation and total internal reflection optics. Shortsequence reads of about tens to a few hundred base pairs are alignedagainst a reference genome and unique mapping of the short sequencereads to the reference genome are identified using specially developeddata analysis pipeline software. After completion of the first read, thetemplates can be regenerated in situ to enable a second read from theopposite end of the fragments. Thus, either single-end or paired endsequencing of the DNA fragments can be used.

Various embodiments of the disclosure may use sequencing by synthesisthat allows paired end sequencing. In some embodiments, the sequencingby synthesis platform by Illumina involves clustering fragments.Clustering is a process in which each fragment molecule is isothermallyamplified. In some embodiments, as the example described here, thefragment has two different adapters attached to the two ends of thefragment, the adapters allowing the fragment to hybridize with the twodifferent oligos on the surface of a flow cell lane. The fragmentfurther includes or is connected to two index sequences at two ends ofthe fragment, which index sequences provide labels to identify differentsamples in multiplex sequencing. In some sequencing platforms, afragment to be sequenced from both ends is also referred to as aninsert.

In some implementation, a flow cell for clustering in the Illuminaplatform is a glass slide with lanes. Each lane is a glass channelcoated with a lawn of two types of oligos (e.g., P5 and P7′ oligos).Hybridization is enabled by the first of the two types of oligos on thesurface. This oligo is complementary to a first adapter on one end ofthe fragment. A polymerase creates a compliment strand of the hybridizedfragment. The double-stranded molecule is denatured, and the originaltemplate strand is washed away. The remaining strand, in parallel withmany other remaining strands, is clonally amplified through bridgeapplication.

In bridge amplification and other sequencing methods involvingclustering, a strand folds over, and a second adapter region on a secondend of the strand hybridizes with the second type of oligos on the flowcell surface. A polymerase generates a complementary strand, forming adouble-stranded bridge molecule. This double-stranded molecule isdenatured resulting in two single-stranded molecules tethered to theflow cell through two different oligos. The process is then repeatedover and over, and occurs simultaneously for millions of clustersresulting in clonal amplification of all the fragments. After bridgeamplification, the reverse strands are cleaved and washed off, leavingonly the forward strands. The 3′ ends are blocked to prevent unwantedpriming.

After clustering, sequencing starts with extending a first sequencingprimer to generate the first read. With each cycle, fluorescently taggednucleotides compete for addition to the growing chain. Only one isincorporated based on the sequence of the template. After the additionof each nucleotide, the cluster is excited by a light source, and acharacteristic fluorescent signal is emitted. The number of cyclesdetermines the length of the read. The emission wavelength and thesignal intensity determine the base call. For a given cluster allidentical strands are read simultaneously. Hundreds of millions ofclusters are sequenced in a massively parallel manner. At the completionof the first read, the read product is washed away.

In the next step of protocols involving two index primers, an index 1primer is introduced and hybridized to an index 1 region on thetemplate. Index regions provide identification of fragments, which isuseful for de-multiplexing samples in a multiplex sequencing process.The index 1 read is generated similar to the first read. Aftercompletion of the index 1 read, the read product is washed away and the3′ end of the strand is de-protected. The template strand then foldsover and binds to a second oligo on the flow cell. An index 2 sequenceis read in the same manner as index 1. Then an index 2 read product iswashed off at the completion of the step.

After reading two indices, read 2 initiates by using polymerases toextend the second flow cell oligos, forming a double-stranded bridge.This double-stranded DNA is denatured, and the 3′ end is blocked. Theoriginal forward strand is cleaved off and washed away, leaving thereverse strand. Read 2 begins with the introduction of a read 2sequencing primer. As with read 1, the sequencing steps are repeateduntil the desired length is achieved. The read 2 product is washed away.This entire process generates millions of reads, representing all thefragments. Sequences from pooled sample libraries are separated based onthe unique indices introduced during sample preparation. For eachsample, reads of similar stretches of base calls are locally clustered.Forward and reversed reads are paired creating contiguous sequences.These contiguous sequences are aligned to the reference genome forvariant identification.

The sequencing by synthesis example described above involves paired endreads, which is used in many of the embodiments of the disclosedmethods. Paired end sequencing involves 2 reads from the two ends of afragment. Paired end reads are used to resolve ambiguous alignments.Paired-end sequencing allows users to choose the length of the insert(or the fragment to be sequenced) and sequence either end of the insert,generating high-quality, alignable sequence data. Because the distancebetween each paired read is known, alignment algorithms can use thisinformation to map reads over repetitive regions more precisely. Thisresults in better alignment of the reads, especially acrossdifficult-to-sequence, repetitive regions of the genome. Paired-endsequencing can detect rearrangements, including insertions and deletions(indels) and inversions.

Paired end reads may use insert of different length (i.e., differentfragment size to be sequenced). As the default meaning in thisdisclosure, paired end reads are used to refer to reads obtained fromvarious insert lengths. In some instances, to distinguish short-insertpaired end reads from long-inserts paired end reads, the latter isspecifically referred to as mate pair reads. In some embodimentsinvolving mate pair reads, two biotin junction adapters first areattached to two ends of a relatively long insert (e.g., several kb). Thebiotin junction adapters then link the two ends of the insert to form acircularized molecule. A sub-fragment encompassing the biotin junctionadapters can then be obtained by further fragmenting the circularizedmolecule. The sub-fragment including the two ends of the originalfragment in opposite sequence order can then be sequenced by the sameprocedure as for short-insert paired end sequencing described above.Further details of mate pair sequencing using an Illumina platform isshown in an online publication at the following address, which isincorporated by reference by its entirety:

res.illumina.com/documents/products/technotes/technote_nextera_matepair_datap_rocessing.pdf

After sequencing of DNA fragments, sequence reads of predeterminedlength, e.g., 100 bp, are localized by mapping (alignment) to a knownreference genome. The mapped reads and their corresponding locations onthe reference sequence are also referred to as tags. In anotherembodiment of the procedure, localization is realized by k-mer sharingand read-read alignment. The analyses of many embodiments disclosedherein make use of reads that are either poorly aligned or cannot bealigned, as well as aligned reads (tags). In one embodiment, thereference genome sequence is the NCBI36/hg18 sequence, which isavailable on the World Wide Web atgenome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).Alternatively, the reference genome sequence is the GRCh37/hg19 orGRCh38, which is available on the World Wide Web atgenome.ucsc.edu/cgi-bin/hgGateway. Other sources of public sequenceinformation include GenBank, dbEST, dbSTS, EMBL (the European MolecularBiology Laboratory), and the DDBJ (the DNA Databank of Japan). A numberof computer algorithms are available for aligning sequences, includingwithout limitation BLAST (Altschul et al., 1990), BLITZ (MPsrch)(Sturrock & Collins, 1993), FASTA (Person & Lipman, 1988), BOWTIE(Langmead et al., Genome Biology 10:R25.1-R25.10 [2009]), or ELAND(Illumina, Inc., San Diego, Calif., USA). In one embodiment, one end ofthe clonally expanded copies of the plasma cfDNA molecules is sequencedand processed by bioinformatics alignment analysis for the IlluminaGenome Analyzer, which uses the Efficient Large-Scale Alignment ofNucleotide Databases (ELAND) software.

In one illustrative, but non-limiting, embodiment, the methods describedherein include obtaining sequence information for the nucleic acids in atest sample, using single molecule sequencing technology of the HelicosTrue Single Molecule Sequencing (tSMS) technology (e.g. as described inHarris T. D. et al., Science 320:106-109 [2008]). In the tSMS technique,a DNA sample is cleaved into strands of approximately 100 to 200nucleotides, and a polyA sequence is added to the 3′ end of each DNAstrand. Each strand is labeled by the addition of a fluorescentlylabeled adenosine nucleotide. The DNA strands are then hybridized to aflow cell, which contains millions of oligo-T capture sites that areimmobilized to the flow cell surface. In certain embodiments thetemplates can be at a density of about 100 million templates/cm². Theflow cell is then loaded into an instrument, e.g., HeliScope™ sequencer,and a laser illuminates the surface of the flow cell, revealing theposition of each template. A CCD camera can map the position of thetemplates on the flow cell surface. The template fluorescent label isthen cleaved and washed away. The sequencing reaction begins byintroducing a DNA polymerase and a fluorescently labeled nucleotide. Theoligo-T nucleic acid serves as a primer. The polymerase incorporates thelabeled nucleotides to the primer in a template directed manner. Thepolymerase and unincorporated nucleotides are removed. The templatesthat have directed incorporation of the fluorescently labeled nucleotideare discerned by imaging the flow cell surface. After imaging, acleavage step removes the fluorescent label, and the process is repeatedwith other fluorescently labeled nucleotides until the desired readlength is achieved. Sequence information is collected with eachnucleotide addition step. Whole genome sequencing by single moleculesequencing technologies excludes or typically obviates PCR-basedamplification in the preparation of the sequencing libraries, and themethods allow for direct measurement of the sample, rather thanmeasurement of copies of that sample.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein include obtaining sequence information for the nucleicacids in the test sample, using the 454 sequencing (Roche) (e.g. asdescribed in Margulies, M. et al. Nature 437:376-380 [2005]). 454sequencing typically involves two steps. In the first step, DNA issheared into fragments of approximately 300-800 base pairs, and thefragments are blunt-ended. Oligonucleotide adapters are then ligated tothe ends of the fragments. The adapters serve as primers foramplification and sequencing of the fragments. The fragments can beattached to DNA capture beads, e.g., streptavidin-coated beads using,e.g., adapter B, which contains 5′-biotin tag. The fragments attached tothe beads are PCR amplified within droplets of an oil-water emulsion.The result is multiple copies of clonally amplified DNA fragments oneach bead. In the second step, the beads are captured in wells (e.g.,picoliter-sized wells). Pyrosequencing is performed on each DNA fragmentin parallel. Addition of one or more nucleotides generates a lightsignal that is recorded by a CCD camera in a sequencing instrument. Thesignal strength is proportional to the number of nucleotidesincorporated. Pyrosequencing makes use of pyrophosphate (PPi) which isreleased upon nucleotide addition. PPi is converted to ATP by ATPsulfurylase in the presence of adenosine 5′ phosphosulfate. Luciferaseuses ATP to convert luciferin to oxyluciferin, and this reactiongenerates light that is measured and analyzed.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein includes obtaining sequence information for the nucleicacids in the test sample, using the SOLiD™ technology (AppliedBiosystems). In SOLiD™ sequencing-by-ligation, genomic DNA is shearedinto fragments, and adapters are attached to the 5′ and 3′ ends of thefragments to generate a fragment library. Alternatively, internaladapters can be introduced by ligating adapters to the 5′ and 3′ ends ofthe fragments, circularizing the fragments, digesting the circularizedfragment to generate an internal adapter, and attaching adapters to the5′ and 3′ ends of the resulting fragments to generate a mate-pairedlibrary. Next, clonal bead populations are prepared in microreactorscontaining beads, primers, template, and PCR components. Following PCR,the templates are denatured and beads are enriched to separate the beadswith extended templates. Templates on the selected beads are subjectedto a 3′ modification that permits bonding to a glass slide. The sequencecan be determined by sequential hybridization and ligation of partiallyrandom oligonucleotides with a central determined base (or pair ofbases) that is identified by a specific fluorophore. After a color isrecorded, the ligated oligonucleotide is cleaved and removed and theprocess is then repeated.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein include obtaining sequence information for the nucleicacids in the test sample, using the single molecule, real-time (SMRT™)sequencing technology of Pacific Biosciences. In SMRT sequencing, thecontinuous incorporation of dye-labeled nucleotides is imaged during DNAsynthesis. Single DNA polymerase molecules are attached to the bottomsurface of individual zero-mode wavelength detectors (ZMW detectors)that obtain sequence information while phospholinked nucleotides arebeing incorporated into the growing primer strand. A ZMW detectorincludes a confinement structure that enables observation ofincorporation of a single nucleotide by DNA polymerase against abackground of fluorescent nucleotides that rapidly diffuse in an out ofthe ZMW (e.g., in microseconds). It typically takes several millisecondsto incorporate a nucleotide into a growing strand. During this time, thefluorescent label is excited and produces a fluorescent signal, and thefluorescent tag is cleaved off. Measurement of the correspondingfluorescence of the dye indicates which base was incorporated. Theprocess is repeated to provide a sequence.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein include obtaining sequence information for the nucleicacids in the test sample, using nanopore sequencing (e.g. as describedin Soni G V and Meller A. Clin Chem 53: 1996-2001 [2007]). Nanoporesequencing DNA analysis techniques are developed by a number ofcompanies, including, for example, Oxford Nanopore Technologies (Oxford,United Kingdom), Sequenom, NABsys, and the like. Nanopore sequencing isa single-molecule sequencing technology whereby a single molecule of DNAis sequenced directly as it passes through a nanopore. A nanopore is asmall hole, typically of the order of 1 nanometer in diameter. Immersionof a nanopore in a conducting fluid and application of a potential(voltage) across it results in a slight electrical current due toconduction of ions through the nanopore. The amount of current thatflows is sensitive to the size and shape of the nanopore. As a DNAmolecule passes through a nanopore, each nucleotide on the DNA moleculeobstructs the nanopore to a different degree, changing the magnitude ofthe current through the nanopore in different degrees. Thus, this changein the current as the DNA molecule passes through the nanopore providesa read of the DNA sequence.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein includes obtaining sequence information for the nucleicacids in the test sample, using the chemical-sensitive field effecttransistor (chemFET) array (e.g., as described in U.S. PatentApplication Publication No. 2009/0026082). In one example of thistechnique, DNA molecules can be placed into reaction chambers, and thetemplate molecules can be hybridized to a sequencing primer bound to apolymerase. Incorporation of one or more triphosphates into a newnucleic acid strand at the 3′ end of the sequencing primer can bediscerned as a change in current by a chemFET. An array can havemultiple chemFET sensors. In another example, single nucleic acids canbe attached to beads, and the nucleic acids can be amplified on thebead, and the individual beads can be transferred to individual reactionchambers on a chemFET array, with each chamber having a chemFET sensor,and the nucleic acids can be sequenced.

In another embodiment, the DNA sequencing technology is the Ion Torrentsingle molecule sequencing, which pairs semiconductor technology with asimple sequencing chemistry to directly translate chemically encodedinformation (A, C, G, T) into digital information (0, 1) on asemiconductor chip. In nature, when a nucleotide is incorporated into astrand of DNA by a polymerase, a hydrogen ion is released as abyproduct. Ion Torrent uses a high-density array of micro-machined wellsto perform this biochemical process in a massively parallel way. Eachwell holds a different DNA molecule. Beneath the wells is anion-sensitive layer and beneath that an ion sensor. When a nucleotide,for example a C, is added to a DNA template and is then incorporatedinto a strand of DNA, a hydrogen ion will be released. The charge fromthat ion will change the pH of the solution, which can be detected byIon Torrent's ion sensor. The sequencer—essentially the world's smallestsolid-state pH meter—calls the base, going directly from chemicalinformation to digital information. The Ion personal Genome Machine(PGM™) sequencer then sequentially floods the chip with one nucleotideafter another. If the next nucleotide that floods the chip is not amatch. No voltage change will be recorded and no base will be called. Ifthere are two identical bases on the DNA strand, the voltage will bedouble, and the chip will record two identical bases called. Directdetection allows recordation of nucleotide incorporation in seconds.

In another embodiment, the present method includes obtaining sequenceinformation for the nucleic acids in the test sample, using sequencingby hybridization. Sequencing-by-hybridization includes contacting theplurality of polynucleotide sequences with a plurality of polynucleotideprobes, wherein each of the plurality of polynucleotide probes can beoptionally tethered to a substrate. The substrate might be flat surfaceincluding an array of known nucleotide sequences. The pattern ofhybridization to the array can be used to determine the polynucleotidesequences present in the sample. In other embodiments, each probe istethered to a bead, e.g., a magnetic bead or the like. Hybridization tothe beads can be determined and used to identify the plurality ofpolynucleotide sequences within the sample.

In some embodiments of the methods described herein, the sequence readsare about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp,about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp,about 100 bp, about 110 bp, about 120 bp, about 130, about 140 bp, about150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about400 bp, about 450 bp, or about 500 bp. It is expected that technologicaladvances will enable single-end reads of greater than 500 bp enablingfor reads of greater than about 1000 bp when paired end reads aregenerated. In some embodiments, paired end reads are used to determinesequences of interest, which include sequence reads that are about 20 bpto 1000 bp, about 50 bp to 500 bp, or 80 bp to 150 bp. In variousembodiments, the paired end reads are used to evaluate a sequence ofinterest. The sequence of interest is longer than the reads. In someembodiments, the sequence of interest is longer than about 100 bp, 500bp, 1000 bp, or 4000 bp. Mapping of the sequence reads is achieved bycomparing the sequence of the reads with the sequence of the referenceto determine the chromosomal origin of the sequenced nucleic acidmolecule, and specific genetic sequence information is not needed. Asmall degree of mismatch (0-2 mismatches per read) may be allowed toaccount for minor polymorphisms that may exist between the referencegenome and the genomes in the mixed sample. In some embodiments, readsthat are aligned to the reference sequence are used as anchor reads, andreads paired to anchor reads but cannot align or poorly align to thereference are used as anchored reads. In some embodiments, poorlyaligned reads may have a relatively large number of percentage ofmismatches per read, e.g., at least about 5%, at least about 10%, atleast about 15%, or at least about 20% mismatches per read.

A plurality of sequence tags (i.e., reads aligned to a referencesequence) are typically obtained per sample. In some embodiments, atleast about 3×10⁶ sequence tags, at least about 5×10⁶ sequence tags, atleast about 8×10⁶ sequence tags, at least about 10×10⁶ sequence tags, atleast about 15×10⁶ sequence tags, at least about 20×10⁶ sequence tags,at least about 30×10⁶ sequence tags, at least about 40×10⁶ sequencetags, or at least about 50×10⁶ sequence tags of, e.g., 100 bp, areobtained from mapping the reads to the reference genome per sample. Insome embodiments, all the sequence reads are mapped to all regions ofthe reference genome, providing genome-wide reads. In other embodiments,reads mapped to a sequence of interest.

Apparatus and Systems for Sequencing Using UMIs

Analysis of the sequencing data and the diagnosis derived therefrom aretypically performed using various computer executed algorithms andprograms. Therefore, certain embodiments employ processes involving datastored in or transferred through one or more computer systems or otherprocessing systems. Embodiments disclosed herein also relate toapparatus for performing these operations. This apparatus may bespecially constructed for the required purposes, or it may be ageneral-purpose computer (or a group of computers) selectively activatedor reconfigured by a computer program and/or data structure stored inthe computer. In some embodiments, a group of processors performs someor all of the recited analytical operations collaboratively (e.g., via anetwork or cloud computing) and/or in parallel. A processor or group ofprocessors for performing the methods described herein may be of varioustypes including microcontrollers and microprocessors such asprogrammable devices (e.g., CPLDs and FPGAs) and non-programmabledevices such as gate array ASICs or general purpose microprocessors.

One implementation provides a system for use in determining a sequencewith low allele frequency in a test sample including nucleic acids, thesystem including a sequencer for receiving a nucleic acid sample andproviding nucleic acid sequence information from the sample; aprocessor; and a machine readable storage medium having stored thereoninstructions for execution on said processor to determine a sequence ofinterest in the test sample by: (a) receiving sequences of a pluralityof amplified polynucleotides, wherein the plurality of amplifiedpolynucleotides are obtained by amplifying double-stranded DNA fragmentsin the sample including the sequence of interest and attaching adaptersto the double-stranded DNA fragments; (b) identifying a plurality ofphysical UMIs that each are found in one of the plurality of amplifiedpolynucleotides, wherein each physical UMI derives from an adapterattached to one of the double-stranded DNA fragments; (c) identifying aplurality of virtual UMIs that each are found in one of the plurality ofamplified polynucleotides, wherein each virtual UMI derives from anindividual molecule of one of the double-stranded DNA fragments; and (d)determining sequences of the double-stranded DNA fragments using thesequences of the plurality of amplified polynucleotides, the pluralityof physical UMIs, and the plurality of virtual UMIs, thereby reducingerrors in the determined sequences of the double-stranded DNA fragments.

Another implementation provides a system including a sequencer forreceiving a nucleic acid sample and providing nucleic acid sequenceinformation from the sample; a processor; and a machine readable storagemedium having stored thereon instructions for execution on saidprocessor to determine a sequence of interest in the test sample. Theinstructions includes: (a) applying adapters to both ends of DNAfragments in the sample, wherein the adapters each include adouble-stranded hybridized region, a single-stranded 5′ arm, asingle-stranded 3′ arm, and a nonrandom unique molecular index (UMI) onone strand or each strand of the adapters, thereby obtaining DNA-adapterproducts; (b) amplifying the DNA-adapter products to obtain a pluralityof amplified polynucleotides; (c) sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads associated witha plurality of nonrandom UMIs; (d) from the plurality of reads,identifying reads sharing a common nonrandom UMI; and (e) from theidentified reads sharing the common nonrandom UMI, determining thesequence of at least a portion of a DNA fragment, from the sample,having an applied adaptor with the common non-random UMI. In someimplementations, the instructions further includes: from the readssharing the common nonrandom UMI, selecting reads sharing both thecommon nonrandom UMI and a common read position, and wherein determiningthe sequence of the DNA fragment in (e) uses only reads sharing both thecommon nonrandom UMI and the common read position in a referencesequence.

In another implementation, the instructions includes: (a) applyingadapters to both ends of double-stranded DNA fragments in the sample,wherein the adapters each include a double-stranded hybridized region, asingle-stranded 5′ arm, a single-stranded 3′ arm, and a nonrandom uniquemolecular index (UMI) on one strand or each strand of the adapters,thereby obtaining DNA-adapter products, wherein the nonrandom UMI can becombined with other information to uniquely identify an individualmolecule of the double-stranded DNA fragments; (b) amplifying bothstrands of the DNA-adapter products to obtain a plurality of amplifiedpolynucleotides; (c) sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads each associatedwith a nonrandom UMI; (d) identifying a plurality of nonrandom UMIsassociated with the plurality of reads; and (e) using the plurality ofreads and the plurality of nonrandom UMIs to determine sequences of thedouble-stranded DNA fragments in the sample.

In some embodiments of any of the systems provided herein, the sequenceris configured to perform next generation sequencing (NGS). In someembodiments, the sequencer is configured to perform massively parallelsequencing using sequencing-by-synthesis with reversible dyeterminators. In other embodiments, the sequencer is configured toperform sequencing-by-ligation. In yet other embodiments, the sequenceris configured to perform single molecule sequencing.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude program instructions and/or data (including data structures) forperforming various computer-implemented operations. Examples ofcomputer-readable media include, but are not limited to, semiconductormemory devices, magnetic media such as disk drives, magnetic tape,optical media such as CDs, magneto-optical media, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and random access memory (RAM).The computer readable media may be directly controlled by an end user orthe media may be indirectly controlled by the end user. Examples ofdirectly controlled media include the media located at a user facilityand/or media that are not shared with other entities. Examples ofindirectly controlled media include media that is indirectly accessibleto the user via an external network and/or via a service providingshared resources such as the “cloud.” Examples of program instructionsinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter.

In various embodiments, the data or information employed in thedisclosed methods and apparatus is provided in an electronic format.Such data or information may include reads and tags derived from anucleic acid sample, reference sequences (including reference sequencesproviding solely or primarily polymorphisms), calls such as cancerdiagnosis calls, counseling recommendations, diagnoses, and the like. Asused herein, data or other information provided in electronic format isavailable for storage on a machine and transmission between machines.Conventionally, data in electronic format is provided digitally and maybe stored as bits and/or bytes in various data structures, lists,databases, etc. The data may be embodied electronically, optically, etc.

One embodiment provides a computer program product for generating anoutput indicating the sequence of a DNA fragment of interest in a testsample. The computer product may contain instructions for performing anyone or more of the above-described methods for determining a sequence ofinterest. As explained, the computer product may include anon-transitory and/or tangible computer readable medium having acomputer executable or compilable logic (e.g., instructions) recordedthereon for enabling a processor to determine a sequence of interest. Inone example, the computer product includes a computer readable mediumhaving a computer executable or compilable logic (e.g., instructions)recorded thereon for enabling a processor to diagnose a condition ordetermine a nucleic acid sequence of interest.

It should be understood that it is not practical, or even possible inmost cases, for an unaided human being to perform the computationaloperations of the methods disclosed herein. For example, mapping asingle 30 bp read from a sample to any one of the human chromosomesmight require years of effort without the assistance of a computationalapparatus. Of course, the problem is compounded because reliable callsof low allele frequency mutations generally require mapping thousands(e.g., at least about 10,000) or even millions of reads to one or morechromosomes.

The methods disclosed herein can be performed using a system fordetermining a sequence of interest in a test sample. The system mayinclude: (a) a sequencer for receiving nucleic acids from the testsample providing nucleic acid sequence information from the sample; (b)a processor; and (c) one or more computer-readable storage media havingstored thereon instructions for execution on said processor todetermining a sequence of interest in the test sample. In someembodiments, the methods are instructed by a computer-readable mediumhaving stored thereon computer-readable instructions for carrying out amethod for determining the sequence of interest. Thus one embodimentprovides a computer program product including a non-transitory machinereadable medium storing program code that, when executed by one or moreprocessors of a computer system, causes the computer system to implementa method for determining the sequences of nucleic acid fragments in atest sample. The program code may include: (a) code for receivingsequences of a plurality of amplified polynucleotides, wherein theplurality of amplified polynucleotides are obtained by amplifyingdouble-stranded DNA fragments in the sample including the sequence ofinterest and attaching adapters to the double-stranded DNA fragments;(b) code for identifying a plurality of physical UMIs that each arefound in one of the plurality of amplified polynucleotides, wherein eachphysical UMI derives from an adapter attached to one of thedouble-stranded DNA fragments; (c) code for identifying a plurality ofvirtual UMIs that each are found in one of the plurality of amplifiedpolynucleotides, wherein each virtual UMI derives from an individualmolecule of one of the double-stranded DNA fragments; and (d) code fordetermining sequences of the double-stranded DNA fragments using thesequences of the plurality of amplified polynucleotides, the pluralityof physical UMIs, and the plurality of virtual UMIs, thereby reducingerrors in the determined sequences of the double-stranded DNA fragments.

In some implementations, the physical UMIs include nonrandom UMIs. Inother implementations, the physical UMIs include random UMIs.

Another implementation provides a computer program product including anon-transitory machine readable medium storing program code that, whenexecuted by one or more processors of a computer system, causes thecomputer system to implement a method for determining the sequences ofnucleic acid fragments in a test sample. The program code may include:(a) code for applying adapters to both ends of DNA fragments in thesample, wherein the adapters each include a double-stranded hybridizedregion, a single-stranded 5′ arm, a single-stranded 3′ arm, and anonrandom unique molecular index (UMI) on one strand or each strand ofthe adapters, thereby obtaining DNA-adapter products; (b) code foramplifying the DNA-adapter products to obtain a plurality of amplifiedpolynucleotides; (c) code for sequencing the plurality of amplifiedpolynucleotides, thereby obtaining a plurality of reads associated witha plurality of nonrandom UMIs; (d) code for identifying, from theplurality of reads, read sharing a common nonrandom UMI; and (e) codefor determining, from the identified reads sharing the common nonrandomUMI, the sequence of at least a portion of a DNA fragment, from thesample, having an applied adaptor with the common non-random UMI.

In another implementation, the program codes include: (a) code forapplying adapters to both ends of double-stranded DNA fragments in thesample, wherein the adapters each include a double-stranded hybridizedregion, a single-stranded 5′ arm, a single-stranded 3′ arm, and anonrandom unique molecular index (UMI) on one strand or each strand ofthe adapters, thereby obtaining DNA-adapter products, wherein thenonrandom UMI can be combined with other information to uniquelyidentify an individual molecule of the double-stranded DNA fragments;(b) code for amplifying both strands of the DNA-adapter products toobtain a plurality of amplified polynucleotides; (c) code for sequencingthe plurality of amplified polynucleotides, thereby obtaining aplurality of reads each associated with a nonrandom UMI; (d) identifyinga plurality of nonrandom UMIs associated with the plurality of reads;and (e) code for using the plurality of reads and the plurality ofnonrandom UMIs to determine sequences of the double-stranded DNAfragments in the sample.

In some embodiments, the instructions may further include automaticallyrecording information pertinent to the method. The patient medicalrecord may be maintained by, for example, a laboratory, physician'soffice, a hospital, a health maintenance organization, an insurancecompany, or a personal medical record website. Further, based on theresults of the processor-implemented analysis, the method may furtherinvolve prescribing, initiating, and/or altering treatment of a humansubject from whom the test sample was taken. This may involve performingone or more additional tests or analyses on additional samples takenfrom the subject.

Disclosed methods can also be performed using a computer processingsystem which is adapted or configured to perform a method fordetermining a sequence of interest. One embodiment provides a computerprocessing system which is adapted or configured to perform a method asdescribed herein. In one embodiment, the apparatus includes a sequencingdevice adapted or configured for sequencing at least a portion of thenucleic acid molecules in a sample to obtain the type of sequenceinformation described elsewhere herein. The apparatus may also includecomponents for processing the sample. Such components are describedelsewhere herein.

Sequence or other data, can be input into a computer or stored on acomputer readable medium either directly or indirectly. In oneembodiment, a computer system is directly coupled to a sequencing devicethat reads and/or analyzes sequences of nucleic acids from samples.Sequences or other information from such tools are provided viainterface in the computer system. Alternatively, the sequences processedby system are provided from a sequence storage source such as a databaseor other repository. Once available to the processing apparatus, amemory device or mass storage device buffers or stores, at leasttemporarily, sequences of the nucleic acids. In addition, the memorydevice may store tag counts for various chromosomes or genomes, etc. Thememory may also store various routines and/or programs for analyzing thepresenting the sequence or mapped data. Such programs/routines mayinclude programs for performing statistical analyses, etc.

In one example, a user provides a sample into a sequencing apparatus.Data is collected and/or analyzed by the sequencing apparatus which isconnected to a computer. Software on the computer allows for datacollection and/or analysis. Data can be stored, displayed (via a monitoror other similar device), and/or sent to another location. The computermay be connected to the internet which is used to transmit data to ahandheld device utilized by a remote user (e.g., a physician, scientistor analyst). It is understood that the data can be stored and/oranalyzed prior to transmittal. In some embodiments, raw data iscollected and sent to a remote user or apparatus that will analyzeand/or store the data. Transmittal can occur via the internet, but canalso occur via satellite or other connection. Alternately, data can bestored on a computer-readable medium and the medium can be shipped to anend user (e.g., via mail). The remote user can be in the same or adifferent geographical location including, but not limited to abuilding, city, state, country or continent.

In some embodiments, the methods also include collecting data regardinga plurality of polynucleotide sequences (e.g., reads, tags and/orreference chromosome sequences) and sending the data to a computer orother computational system. For example, the computer can be connectedto laboratory equipment, e.g., a sample collection apparatus, anucleotide amplification apparatus, a nucleotide sequencing apparatus,or a hybridization apparatus. The computer can then collect applicabledata gathered by the laboratory device. The data can be stored on acomputer at any step, e.g., while collected in real time, prior to thesending, during or in conjunction with the sending, or following thesending. The data can be stored on a computer-readable medium that canbe extracted from the computer. The data collected or stored can betransmitted from the computer to a remote location, e.g., via a localnetwork or a wide area network such as the internet. At the remotelocation various operations can be performed on the transmitted data asdescribed below.

Among the types of electronically formatted data that may be stored,transmitted, analyzed, and/or manipulated in systems, apparatus, andmethods disclosed herein are the following:

-   -   Reads obtained by sequencing nucleic acids in a test sample    -   Tags obtained by aligning reads to a reference genome or other        reference sequence or sequences    -   The reference genome or sequence    -   Thresholds for calling a test sample as either affected,        non-affected, or no call    -   The actual calls of medical conditions related to the sequence        of interest    -   Diagnoses (clinical condition associated with the calls)    -   Recommendations for further tests derived from the calls and/or        diagnoses    -   Treatment and/or monitoring plans derived from the calls and/or        diagnoses

These various types of data may be obtained, stored transmitted,analyzed, and/or manipulated at one or more locations using distinctapparatus. The processing options span a wide spectrum. At one end ofthe spectrum, all or much of this information is stored and used at thelocation where the test sample is processed, e.g., a doctor's office orother clinical setting. In other extreme, the sample is obtained at onelocation, it is processed and optionally sequenced at a differentlocation, reads are aligned and calls are made at one or more differentlocations, and diagnoses, recommendations, and/or plans are prepared atstill another location (which may be a location where the sample wasobtained).

In various embodiments, the reads are generated with the sequencingapparatus and then transmitted to a remote site where they are processedto determine a sequence of interest. At this remote location, as anexample, the reads are aligned to a reference sequence to produce anchorand anchored reads. Among the processing operations that may be employedat distinct locations are the following:

-   -   Sample collection    -   Sample processing preliminary to sequencing    -   Sequencing    -   Analyzing sequence data and deriving medical calls    -   Diagnosis    -   Reporting a diagnosis and/or a call to patient or health care        provider    -   Developing a plan for further treatment, testing, and/or        monitoring    -   Executing the plan    -   Counseling

Any one or more of these operations may be automated as describedelsewhere herein. Typically, the sequencing and the analyzing ofsequence data and deriving medical calls will be performedcomputationally. The other operations may be performed manually orautomatically.

FIG. 6 shows one implementation of a dispersed system for producing acall or diagnosis from a test sample. A sample collection location 01 isused for obtaining a test sample from a patient. The samples thenprovided to a processing and sequencing location 03 where the testsample may be processed and sequenced as described above. Location 03includes apparatus for processing the sample as well as apparatus forsequencing the processed sample. The result of the sequencing, asdescribed elsewhere herein, is a collection of reads which are typicallyprovided in an electronic format and provided to a network such as theInternet, which is indicated by reference number 05 in FIG. 6.

The sequence data is provided to a remote location 07 where analysis andcall generation are performed. This location may include one or morepowerful computational devices such as computers or processors. Afterthe computational resources at location 07 have completed their analysisand generated a call from the sequence information received, the call isrelayed back to the network 05. In some implementations, not only is acall generated at location 07 but an associated diagnosis is alsogenerated. The call and or diagnosis are then transmitted across thenetwork and back to the sample collection location 01 as illustrated inFIG. 6. As explained, this is simply one of many variations on how thevarious operations associated with generating a call or diagnosis may bedivided among various locations. One common variant involves providingsample collection and processing and sequencing in a single location.Another variation involves providing processing and sequencing at thesame location as analysis and call generation.

EXPERIMENTAL Example 1 Error Suppression Using Random Physical UMI andVirtual UMI

FIG. 7A and FIG. 7B show experimental data demonstrating theeffectiveness of error suppression using the methods disclosed herein.Experimenters used sheared gDNA of NA12878. They used TruSeq librarypreparation and enrichment with custom panel (˜130 Kb). Sequencing wasperformed at 2×150 bp using HiSeq2500 rapid mode, and mean targetcoverage was ˜10,000×. FIG. 7A shows profile of error rate (allelefrequency of second highest base) of high quality bases (>Q30) usingstandard method (the mean error rate is 0.04%). FIG. 7B shows profile oferror rate of collapsing/UMI pipeline (the mean error rate is 0.007%).Note that these results are based on prototype code, and furtherreduction of error rate may be achieved with refined methods.

Example 2 Error Suppression Using Nonrandom Physical UMI and Position

FIG. 8 shows data indicating that using position information alone tocollapse reads tends to collapse reads that are actually derived fromdifferent source molecules. This phenomenon is also referred to as readcollision. As a result, the method tends to under estimate the number offragments in a sample. Shown on the Y axis of FIG. 8 is the observedfragment counts by collapsing reads using position information alone. Soon the X axis of FIG. 8 is the estimated fragment counts factoring indifferent genotypes such as different SNPs and other genotypicdifferences. As shown in the figure, the observed fragment counts arefewer than the genotype adjusted fragment counts, indicating anunderestimation and read collision using position information alone tocollapse reads and identify fragments.

FIG. 9 plots empirical data showing that using nonrandom UMI andposition information to collapse reads may provide more accurateestimates of fragments than using position information alone. Thenonrandom UMI is a 6 bp, duplex UMI located on the double-stranded endof the adapter, the non-random UMI being selected from one of 96different UMIs. Plotted on the Y axis is the mean collapsed fragmentcount, with the position-based collapsing method on the left of eachpair of bars, and the UMI and position-based collapsing method on theright of each pair of bars. The left three pairs of bars show data forcell free DNA samples of three increasing inputs. The right three pairsof bars show data for three sheared genomic DNA samples. Pairwisecomparisons of the two collapsing methods show that UMI andposition-based collapsing provides higher estimate of fragment countsthan using position alone for collapsing. The comparison of the twocollapsing methods shows larger differences for cell free DNA samplesthan four genomic DNA samples. Furthermore, the difference for cell freeDNA samples increases as the sample input increases. The data suggestthat collapsing using both nonrandom UMI and position information cancorrect for read collision and fragment underestimation, especially forcell free DNA.

FIG. 10 shows different errors occur in three samples processed withrandom UMIs in tabular form. The first three rows of data indicate thepercentages of different types of errors 43 samples. The last row showserror rates averaged across the samples. As shown in the table, 97.58%of the UMIs contain no errors, and 1.07% of the UMIs contain onerecoverable era. Over 98.65% of all the UMIs are usable for indexingindividual DNA fragments. Many of the rest may still be usable whencombined with contextual information.

FIG. 11A shows sensitivity and selectivity of calling somatic mutationand CNV in a gDNA sample using the two collapsing methods with twodifferent tools: VarScan and Denovo, Applied with the VarScan tool,collapsing using both UMI and position information provides slightlyhigher sensitivity and markedly better selectivity (lower false positiverate), as indicated by a shift of the ROC curve to upper left when UMIis used with position. Applied with the Denovo tool, collapsing usingboth UMI and position information provides markedly higher sensitivity.

FIGS. 11B-C show selectivity (i.e., false positive rate) of callingsomatic mutation and CNV in three cfDNA samples having increasing sampleinputs using the two collapsing methods with two different tools:VarScan and Denovo, Applied with the VarScan tool, collapsing using bothUMI and position information provides markedly better selectivity (lowerfalse positive rate) for all three samples. Applied with the Denovotool, collapsing using both UMI and position information provides betterselectivity (lower false alarm rate) only in the sample having thelargest input.

The present disclosure may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the disclosure is, therefore,indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A method for sequencing nucleic acid moleculesfrom a sample using unique molecular indices (UMIs), wherein each uniquemolecular index (UMI) is an oligonucleotide sequence that can be used toidentify an individual molecule of a double-stranded DNA fragment in thesample, comprising (a) applying adapters to both ends of a plurality ofdouble-stranded DNA fragments in the sample to obtain DNA-adapterproducts, wherein each adapter comprises a double-stranded hybridizedregion, a single-stranded 5′ arm, a single-stranded 3′ arm, and aphysical UMI on one strand or each strand of the adapter, the physicalUMI being selected from a plurality of physical UMIs, eachdouble-stranded DNA fragment in the sample comprises a virtual UMI onone strand or each strand of the double-stranded DNA fragment, thevirtual UMI is a sequence of nucleotides shorter than thedouble-stranded DNA fragment, the position of the virtual UMI is definedat or with respect to an end of the double-stranded DNA fragment, andthe plurality of double-stranded DNA fragments is not obtained byrestriction endonuclease digestion; (b) amplifying both strands of theDNA-adapter products to obtain a plurality of amplified polynucleotides;(c) sequencing, using a nucleic acid sequencer, the plurality ofamplified polynucleotides, thereby obtaining a plurality of reads eachcomprising a physical UMI corresponding to a physical UMI on an adapterand a virtual UMI corresponding to a virtual UMI on a double-strandedDNA fragment in the sample; (d) identifying a plurality of physical UMIsequences for the plurality of reads; (e) identifying a plurality ofvirtual UMI sequences for the plurality of reads; and (f) determiningsequences of the plurality of double-stranded DNA fragments in thesample by: (i) grouping the plurality of reads based at least on theplurality of virtual UMI sequences to obtain a plurality of groups ofreads, (ii) determining a plurality of consensus nucleotide sequencesusing the plurality of groups of reads, and (iii) determining thesequences of the plurality of double-stranded DNA fragments using theplurality of consensus nucleotide sequences.
 2. The method of claim 1,wherein (f)(i) comprises: grouping the plurality of reads based at leaston the plurality of virtual UMI sequences and the plurality of physicalUMI sequences in the reads to obtain the plurality of groups of reads,each group having a unique combination of a virtual UMI sequence and aphysical UMI sequence.
 3. The method of claim 1, wherein the pluralityof physical UMIs comprises random UMIs.
 4. The method of claim 1,wherein the plurality of physical UMIs comprises nonrandom UMIs.
 5. Themethod of claim 4, wherein every nonrandom UMI differs from every othernonrandom UMI of the adapters by at least two nucleotides atcorresponding sequence positions of the nonrandom UMIs.
 6. The method ofclaim 5, wherein the plurality of physical UMIs includes no more thanabout 10,000 unique nonrandom UMIs.
 7. The method of claim 6, whereinthe plurality of physical UMIs includes no more than about 1,000 uniquenonrandom UMIs.
 8. The method of claim 7, wherein the plurality ofphysical UMIs includes no more than about 500 unique nonrandom UMIs. 9.The method of claim 8, wherein the plurality of physical UMIs includesno more than about 100 unique nonrandom UMIs.
 10. The method of claim 9,wherein the plurality of physical UMIs includes about 96 uniquenonrandom UMIs.
 11. The method of claim 1, wherein applying adapters toboth ends of double-stranded DNA fragments comprises ligating theadapters to both ends of the double-stranded DNA fragments.
 12. Themethod of claim 1, wherein the plurality of physical UMIs includes fewerthan 12 nucleotides.
 13. The method of claim 12, wherein the pluralityof physical UMIs includes no more than 6 nucleotides.
 14. The method ofclaim 12, wherein the plurality of physical UMIs includes no more than 4nucleotides.
 15. The method of claim 1, wherein the adapters eachcomprise a physical UMI on each strand of the adapters in thedouble-stranded hybridized region.
 16. The method of claim 15, whereinthe physical UMI is at or near an end of the double-stranded hybridizedregion, said end of the double-stranded hybridized region being oppositefrom the 3′ arm or the 5′ arm.
 17. The method of claim 16, wherein thephysical UMI is at said end of the double-stranded hybridized region, oris one nucleotide away from said end of the double-stranded hybridizedregion.
 18. The method of claim 17, wherein the adapters each comprise a5′-TGG-3′ trinucleotide or a 3′-ACC-5′ trinucleotide on thedouble-stranded hybridized region adjacent to a physical UMI.
 19. Themethod of claim 18, wherein the adapters each comprise a read primersequence on each strand of the double-stranded hybridized region. 20.The method of claim 1, wherein the adapters each comprise a physical UMIon only one strand of the adapters on the single-stranded 5′ arm or thesingle-stranded 3′ arm.
 21. The method of claim 20, wherein (f)comprises: (i) collapsing reads having a same first physical UMIsequence into a first group to obtain a first consensus nucleotidesequence; (ii) collapsing reads having a same second physical UMIsequence into a second group to obtain a second consensus nucleotidesequence; and (iii) determining, using the first and second consensusnucleotide sequences, a sequence of one of the double-stranded DNAfragments in the sample.
 22. The method of claim 21, wherein (iii)comprises: (1) obtaining, using localization information and sequenceinformation of the first and second consensus nucleotide sequences, athird consensus nucleotide sequence, and (2) determining, using thethird consensus nucleotide sequence, the sequence of one of thedouble-stranded DNA fragments.
 23. The method of claim 20, wherein (e)comprises identifying the plurality of virtual UMI sequences, while theadapters each comprise the physical UMI on only the single-stranded 5′arm or the single-stranded 3′ arm.
 24. The method of claim 23, wherein(f) comprises: (i) combining reads having a first physical UMI sequenceand at least one virtual UMI sequence in a read direction and readshaving a second physical UMI sequence and the at least one virtual UMIsequence in the read direction to determine a consensus nucleotidesequence; and (ii) determining a sequence of one of the double-strandedDNA fragments in the sample using the consensus nucleotide sequence. 25.The method of claim 1, wherein the adapters each comprise a physical UMIon each strand of the adapters in a double-stranded region of theadapters, wherein the physical UMI on one strand is complementary to thephysical UMI on the other strand.
 26. The method of claim 25, wherein(f) comprises: (i) combining reads having a first physical UMI sequence,at least one virtual UMI sequence, and a second physical UMI sequence inthe 5′ to 3′ direction and reads having the second physical UMIsequence, the at least one virtual UMI sequence, and the first physicalUMI sequence in the 5′ to 3′ direction to determine a consensusnucleotide sequence; and (ii) determining a sequence of one of thedouble-stranded DNA fragments in the sample using the consensusnucleotide sequence.
 27. The method of claim 1, wherein the adapterseach comprise a first physical UMI on a 3′ arm of the adapter and asecond physical UMI on a 5′ arm of the adapter, wherein the firstphysical UMI and the second physical UMI are not complementary to eachother.
 28. The method of claim 27, wherein (f) comprises: (i) combiningreads having a first physical UMI sequence, at least one virtual UMIsequence, and a second physical UMI sequence in the 5′ to 3′ directionand reads having a third physical UMI sequence, the at least one virtualUMI sequence, and a fourth physical UMI sequence in the 5′ to 3′direction to determine a consensus nucleotide sequence; and (ii)determining a sequence of one of the double-stranded DNA fragments inthe sample using the consensus nucleotide sequence.
 29. The method ofclaim 1, wherein at least some of the virtual UMIs derive fromsubsequences at or near the ends of the double-stranded DNA fragments inthe sample.
 30. The method of claim 1, wherein one or more physical UMIsand/or one or more virtual UMIs are uniquely associated with adouble-stranded DNA fragment in the sample.
 31. The method of claim 1,wherein the double-stranded DNA fragments in the sample comprise morethan about 1,000 DNA fragments.
 32. The method of claim 1, wherein theplurality of virtual UMI sequences comprise about 6 bp to about 24 bp.33. The method of claim 32, wherein the plurality of virtual UMIsequences comprise about 6 bp to about 10 bp.
 34. The method of claim 1,wherein obtaining the plurality of reads in operation (c) comprises:obtaining two pair-end reads from each of the amplified polynucleotides,wherein the two pair-end reads comprise a long read and a short read,the long read being longer than the short read.
 35. The method of claim34, wherein (f) comprises: combining read pairs comprising a firstphysical UMI sequence into a first group and combining read pairscomprising a second physical UMI sequence into a second group, whereinthe first and the second physical UMI sequences are uniquely associatedwith a double-stranded fragment in the sample; and determining thesequence of the double-stranded fragment in the sample using sequenceinformation of long reads in the first group and sequence information oflong reads in the second group.
 36. The method of claim 34, and whereinthe long read has a read length of about 500 bp or more.
 37. The methodof claim 34, and wherein the short read has a read length of about 50 bpor less.
 38. The method of claim 1, wherein the method suppresses errorsarise in one or more of the following operations: PCR, librarypreparation, clustering, and sequencing.
 39. The method of claim 1,wherein the amplified polynucleotides include an allele having an allelefrequency lower than about 1%.
 40. The method of claim 39, wherein theamplified polynucleotides include a cell free DNA molecule originatingfrom a tumor, and the allele is indicative of the tumor.
 41. The methodof claim 1, wherein sequencing the plurality of amplifiedpolynucleotides comprises obtaining reads having at least about 100 bp.42. The method of claim 1, wherein the virtual UMI is at or near an endof the DNA fragment in the sample.
 43. The method of claim 1, whereinthe plurality of double-stranded DNA fragments is obtained by randomfragmentation.
 44. The method of claim 43, wherein the randomfragmentation is selected from the group comprising of: shearing,sonication, nebulization, limited DNAse digestion, alkali treatment, andany combinations thereof.
 45. The method of claim 1, wherein theplurality of double-stranded DNA fragments comprises circulatingcell-free DNA or circulating tumor DNA.
 46. A method for sequencingnucleic acid molecules from a sample using unique molecular indices(UMIs), wherein each unique molecular index (UMI) is an oligonucleotidesequence that can be used to identify an individual molecule of adouble-stranded DNA fragment in the sample, comprising (a) applyingadapters to both ends of a plurality of double-stranded DNA fragments inthe sample to obtain DNA-adapter products, wherein each adaptercomprises a double-stranded hybridized region, a single-stranded 5′ arm,a single-stranded 3′ arm, and a physical UMI on one strand or eachstrand of the adapter, the physical UMI being selected from a pluralityof physical UMIs, each double-stranded DNA fragment in the samplecomprises a virtual UMI on one strand or each strand of thedouble-stranded DNA fragment, the virtual UMI is a sequence ofnucleotides shorter than the double-stranded DNA fragment, the positionof the virtual UMI is defined at or with respect to an end of thedouble-stranded DNA fragment, and the plurality of double-stranded DNAfragments is obtained by random fragmentation; (b) amplifying bothstrands of the DNA-adapter products to obtain a plurality of amplifiedpolynucleotides; (c) sequencing, using a nucleic acid sequencer, theplurality of amplified polynucleotides, thereby obtaining a plurality ofreads each comprising a physical UMI corresponding to a physical UMI onan adapter and a virtual UMI corresponding to a virtual UMI on adouble-stranded DNA fragment in the sample; (d) identifying a pluralityof physical UMI sequences for the plurality of reads; (e) identifying aplurality of virtual UMI sequences for the plurality of reads; and (f)determining sequences of the plurality of double-stranded DNA fragmentsin the sample by: (i) grouping the plurality of reads based at least onthe plurality of virtual UMI sequences to obtain a plurality of groupsof reads, (ii) determining a plurality of consensus nucleotide sequencesusing the plurality of groups of reads, and (iii) determining thesequences of the plurality of double-stranded DNA fragments using theplurality of consensus nucleotide sequences.